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Record W2607696204 · doi:10.2527/asasann.2017.256

256 Effects of post-partum lipid supplementation and source of supplemental lipid on reproductive performance of lactating beef cows grazing cool-season grass pastures

2017· article· en· W2607696204 on OpenAlexaff
F. Añez-Osuna, G.B. Penner, John Campbell, Daalkhaijav Damiran, P. G. Jefferson, H.A. Lardner, J. J. McKinnon

Bibliographic record

VenueJournal of Animal Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAnimal scienceIce calvingGrazingRandomized block designForageBiologyBeef cattleCanolaCompletely randomized designLactationAgronomyPregnancy

Abstract

fetched live from OpenAlex

A three-year study was conducted to evaluate the effects of post-partum lipid supplementation and source of supplemental lipid on performance of lactating beef cows grazing cool-season grass (CSG) pastures. Each year, 36 second- and third-calving Angus cows with calves were stratified by BW, BCS (Scottish System: 1 to 5), and days post-partum (38 ± 1.5 d) and randomly assigned to 9 paddocks (4 cows/pen) of long-established CSG pastures. Subsequently, each paddock was randomly assigned to one of three replicated (n = 3) treatments: a non-supplemented control (CON) treatment and two supplemented (SUP) treatments where cows were offered either a canola seed (CAN; 9.1 ± 0.68% EE) or a flaxseed (FLX; 8.5 ± 0.38% EE) based pellet. Pelleted supplements were offered daily, and amounts were such that each paddock received 1.2 kg/d (300 g/cow/d) of lipids (EE) from supplement. Each year, the supplementation period was 42 d, after which all cows were managed in a single group and exposed to a 63 d breeding season (1:18 bull:cow). Data were analyzed as a randomized complete block design with contrasts for the effect of lipid supplementation (CON vs. SUP) and source of lipids (CAN vs. FLX). At the start of trial, no difference (P ≥ 0.42) was observed among treatments for BW (554 ± 3.0 kg), BCS (95 ± 0.02% of optimal cows), proportion of cows cycling (39 ± 4.7%), and available CSG forage (1977 ± 105.7 kg/ha). Over the 42 d of supplementation period, no difference (P ≥ 0.56) was observed among treatments for nutrient composition of CSG pastures (12.5 ± 0.24% CP and 41.5 ± 0.45% ADF). However, CON had lower (P = 0.04) residual forage (806 ± 118.8 kg/ha) and tended (P = 0.06) to have greater forage utilization (61 ± 4.3%) compared to SUP, while no difference (P ≥ 0.34) was observed between CAN and FLX (904 ± 88.8 vs. 971 ± 88.8 kg/ha and 50 ± 3.6 vs. 52 ± 3.8%). At the end of trial, no difference (P ≥ 0.69) was observed among treatments for BW (578 ± 7.6 kg), ADG (0.6 ± 0.12 kg/d), BCS (99 ± 0.01% of optimal cows), and proportion of cows cycling (88 ± 3.1%). Forty-five d after the end of breeding season, no difference (P = 0.97) was observed among treatments for conception rate (97 ± 1.6%). These results show that supplementing lipids to second- and third-calving beef cows prior to breeding has no effect on their reproductive performance. However, the lower forage utilization shown for supplemented cows suggests that this supplementation strategy might be suitable under limited forage and/or high stocking rates scenarios.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

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 head0.016
GPT teacher head0.272
Teacher spread0.257 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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