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Record W2561279468 · doi:10.3168/jds.2016-11776

Potassium carbonate as a cation source for early-lactation dairy cows fed high-concentrate diets

2016· article· en· W2561279468 on OpenAlexafffund
A.R. Alfonso-Avila, Édith Charbonneau, P.Y. Chouinard, Gaëtan F. Tremblay, R. Gervais

Bibliographic record

VenueJournal of Dairy Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food CanadaUniversité Laval
FundersAgriculture and Agri-Food CanadaMinistère de l'Agriculture et de l'AlimentationFonds de recherche du Québec – Nature et technologiesUniversité LavalNovalaitMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsChemistryRandomized block designLactationDry matterSilageAnimal scienceRumenForageMilk fatFood scienceAgronomyBiology

Abstract

fetched live from OpenAlex

Previous studies reported that addition of K 2 CO 3 to high-concentrate diets improved milk fat synthesis, although the mechanism is yet to be established. The objective of the current experiment was to investigate the effects of dietary cation-anion difference (DCAD), cation source, and buffering ability of the mineral supplement on rumen biohydrogenation of fatty acids and production performance in dairy cows fed a high-concentrate diet. Thirty-five early-lactation Holstein cows (25 multiparous ruminally fistulated and 10 primiparous nonfistulated) were used in a randomized complete block design (7 blocks) with 33-d periods, including a 5-d pre-treatment collection period used as a covariate. Diets were (1) control, a basal diet [47% nonfibrous carbohydrates, DCAD (Na + K – Cl – S) = 65 mEq/kg of dry matter (DM)] containing 40% forage (including 60% corn silage) and 60% concentrate, (2) K 2 CO 3 (control + K 2 CO 3 , 1.8% of DM, DCAD=326 mEq/kg of DM), (3) KHCO 3 (control + KHCO 3 , 2.6% of DM, DCAD=324 mEq/kg of DM), (4) KCl (control + KCl, 2.0% of DM, DCAD=64 mEq/kg of DM), and (5) Na 2 CO 3 (control + Na 2 CO 3 , 1.4% of DM, DCAD=322 mEq/kg of DM). Pre-planned orthogonal contrasts were used to assess the effects of K 2 CO 3 (control vs. K 2 CO 3 ), buffering ability (K 2 CO 3 vs. KHCO 3 ), DCAD (K 2 CO 3 vs. KCl), and cation type (K 2 CO 3 vs. Na 2 CO 3 ). Supplementing K 2 CO 3 in a high-concentrate diet did not improve milk fat yield or 4% fat-corrected milk yield. Milk fat concentration was greater in cows fed K 2 CO 3 compared with control (4.03 vs. 3.26%). Milk yield tended to decrease (34.5 vs. 38.8 kg/d) and lactose yield decreased in cows fed K 2 CO 3 as compared with KCl (1.64 vs. 1.87 kg/d). Milk fat concentration of trans -10 18:1 was increased when cows were fed Na 2 CO 3 as compared with K 2 CO 3 . A positive relationship was observed between concentrations of anteiso 15:0 and trans -10, cis -12 18:2 in milk fat from cows receiving K 2 CO 3 . Milk Na concentration was increased, whereas milk Cl was decreased with K 2 CO 3 as compared with KHCO 3 or KCl. A positive relationship was established between milk Cl concentration and milk yield (R 2 = 0.34) across all dietary treatments. Cation-anion difference (Na + K – Cl – S) in ruminal fluid was increased with K 2 CO 3 as compared with control or KCl. Blood pH tended to decrease in cows fed KCl compared with K 2 CO 3 . Our results suggest that mineral supplementation tends to affect milk and milk fat synthesis and that factors other than DCAD, potassium ion, or buffer ability may be implicated. The variations observed in mineral composition of milk suggest an allostatic process to maintain an ionic equilibrium in mammary epithelial cells in response to mineral composition of the diet.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

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)0.0010.000

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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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 designBench or experimental
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

Citations14
Published2016
Admission routes2
Has abstractyes

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