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Record W4387766145 · doi:10.3168/jds.2023-23589

Dietary supplementation of vitamin D3 and calcium partially recover the compromised time budget and circadian rhythm of lying behavior in lactating cows under heat stress

2023· article· en· W4387766145 on OpenAlexafffund
Kai Wang, Alexis Ruíz-González, S.E. Räisänen, V. Ouellet, A. Boucher, D.E. Rico, Mutian Niu

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversité LavalCentre de Recherche en Sciences Animales de Deschambault
FundersAgriculture and Agri-Food CanadaMitacsEidgenössische Technische Hochschule Zürich
KeywordsVitaminAnimal scienceChemistryCircadian rhythmEndocrinologyVitamin D and neurologyInternal medicineLatin squareCalciumDairy cattleBiologyMedicineFood scienceRumenFermentation

Abstract

fetched live from OpenAlex

Heat stress impedes cattle behavior and performance and is an animal comfort and welfare issue. The objective of this study was to characterize the time budget and circadian rhythm of lying behavior in dairy cows during heat stress and to assess the effect of dietary supplementation of vitamin D 3 and Ca. Twelve multiparous Holstein cows (42.2 ± 5.6 kg milk/d; 83 ± 27 d in milk) housed in tie-stalls were used in a split-plot design with the concentration of dietary vitamin E and Se as main plots (LESe: 11.1 IU/kg and 0.55 mg/kg, and HESe: 223 IU/kg and 1.8 mg/kg, respectively). Within each plot cows were randomly assigned to: 1) heat stress with low concentrations of vitamin D 3 and Ca (HS, 1012 IU/kg and 0.73%, respectively), 2) heat stress with high concentrations of vitamin D 3 and Ca (HS+D 3 /Ca; 3,764 IU/kg and 0.97%, respectively), or 3) thermoneutral pair-fed (TNPF) with low concentrations of vitamin D 3 and Ca (1012 IU/kg and 0.73%, respectively) in a Latin Square design with 14-d periods and 7-d washouts. Lying behavior was measured with HOBO® Loggers in 15-min intervals. Overall, cows in HS spent less time lying per day relative to TNPF from d 7 to 14. Daily lying time was positively correlated with milk yield, energy-corrected milk yield, and feed efficiency, and was negatively correlated with rectal temperature, respiratory rate, fecal calprotectin, tumor necrosis factor-α, and C-reactive protein. A treatment by time interaction was observed for lying behavior: the time spent lying was lesser for cows in HS than in TNPF in the early morning (0000 – 0600 h) and in the night (1800 – 2400 h). The circadian rhythm of lying behavior was characterized by fitting a cosine function of time into linear mixed model. Daily rhythmicity of lying was detected for cows in TNPF and HS+D 3 /Ca, whereas only a tendency in HS cows was observed. Cows in TNPF had the highest mesor (the average level of diurnal fluctuations; 34.2 min/h) and amplitude (the distance between the peak and mesor; 17.9 min/h). Both the mesor and amplitude were higher in HS+D 3 /Ca relative to HS (26.6 vs. 25.2 min/h and 3.91 min/h vs. 2.18 min/h, respectively). The acrophase (time of the peak) of lying time in TNPF, HS, and HS+D 3 /Ca were 0028, 0152, and 0054 h, respectively. Lastly, a continuous increase in daily lying time in TNPF was observed during the first 4 d of the experimental period in which DMI was gradually restricted, suggesting that intake restrictions may shift feeding behavior and introduce biases in the behavior of animals. In conclusion, lying behavior was compromised in dairy cows under heat stress, characterizing reduced daily lying time and disrupted circadian rhythms, and the compromised lying behavior can be partially restored by supplementation of vitamin D 3 and Ca. Further research may be required for a more suitable model to study behavior of cows under heat stress.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 head0.025
GPT teacher head0.267
Teacher spread0.242 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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