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Record W4205506813 · doi:10.3168/jds.2021-20457

Effect of dietary supplementation or cessation of magnesium-based alkalizers on milk fat output in dairy cows under milk fat depression conditions

2022· article· en· W4205506813 on OpenAlexaff
A. Razzaghi, Alireza Vakili, Behzad Khorrami, Mohtasham Ghaffari, D.E. Rico

Bibliographic record

VenueJournal of Dairy Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsCentre de Recherche en Sciences Animales de Deschambault
FundersFerdowsi University of Mashhad
KeywordsLactoseLactationDry matterMagnesiumChemistryFood scienceDairy cattleFatty acidAnimal scienceRandomized block designBiologyAgronomyBiochemistryPregnancy

Abstract

fetched live from OpenAlex

We aimed to evaluate the effects of dietary supplementation with magnesium oxide and calcium-magnesium dolomite on milk fat synthesis and milk fatty acid profile or persistency in milk fat synthesis after their cessation in dairy cows under milk fat depression conditions. Twenty-four multiparous dairy cows in early lactation (mean ± standard deviation; 112 ± 14 d in milk) were used in a randomized complete block design. Milk fat depression was induced in all cows for 10 d by feeding a diet containing 35.2% starch, 28.7% neutral detergent fiber, and 4.8% total fatty acid (dry matter). The experiment was conducted in 2 periods. During the Mg-supplementation period (d 1-20), cows were randomly assigned to (1) the milk fat depression diet used during the induction phase (control; n = 8), (2) the control diet plus 0.4% magnesium oxide (MG; n = 8), or (3) the control diet plus 0.8% calcium-magnesium dolomite (CMC; n = 8). Compared with the control group, feeding the magnesium-supplemented diets increased milk fat concentration and yield by 12% within 4 d. During the 20-d Mg-supplementation period, both the MG and CMC diets increased milk fat concentration and yield, as well as 3.5% fat-corrected milk and energy-corrected milk yield, without affecting dry matter intake, milk yield, and milk protein and lactose concentrations. In the Mg-cessation period (d 21-30), all cows received the control diet, which resulted in a greater milk fat concentration and yield in the cows that had already received the MG and CMC diets in the Mg-supplementation period. Whereas, milk fat concentration and yield remained high after discontinuation of the magnesium-containing alkalizer until d 27. The difference in milk fat synthesis was associated with lower trans-10 C18:1 (-22%) and higher trans-11 C18:1 (+12.5%) concentrations in milk during the Mg-supplementation period. Furthermore, it was evident that within 2 d of supplementation, the trans-10:trans-11 ratio was lower in MG and CMC cows compared with cows receiving the control. This suggested that the effect of magnesium-based alkalizers on milk fat synthesis was mediated via a shift in ruminal biohydrogenation of cis-9,cis-12 C18:2 in the rumen. In conclusion, abrupt addition of magnesium oxide and calcium-magnesium dolomite increased milk fat synthesis, which persisted for 7 d after cessation of magnesium-based alkalizers. A similar ability to recover milk fat synthesis and normal fatty acid biohydrogenation pathways was observed for magnesium oxide and calcium-magnesium dolomite.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.032
GPT teacher head0.300
Teacher spread0.268 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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