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Record W7066228021

Feed delivery method affects the learning of feeding and competitive behaviour in dairy heifers.

2013· article· en· W7066228021 on OpenAlexfundno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsnot available
FundersMinistry of Agriculture, Food and Rural AffairsNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural AffairsDairy Farmers of Canada
KeywordsFecesDry matterTotal mixed rationFeeding behaviorSortingDairy cattle
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to determine how different feeding methods may affect the learning of feeding, sorting, and competitive behavior of growing dairy heifers. We hypothesized that heifers previously fed a total mixed ration (TMR) would distribute their feeding time more evenly throughout the day, sort the new ration less, compete less for feed, maintain a more solid fecal consistency, and continue to grow rapidly compared with heifers previously fed a top-dressed ration (TDR). Thirty-two Holstein heifers (237.2 ± 21.9 d of age) were divided into 8 groups of 4 and exposed to 1 of 2 treatments for 13 wk: 1) TMR or 2) TDR, with each containing 65% grass/alfalfa haylage and 35% textured concentrate on a dry matter (DM) basis. Following this feeding period, all heifers were switched to an unfamiliar TMR containing 56.1% grass/alfalfa haylage, 21.0% corn silage, 21.0% high-moisture corn, and 1.9% mineral supplement (DM basis) for 7 wk. Group DM intakes were recorded daily throughout the experiment. Feeding behavior, recorded using time-lapse video, and sorting behavior were measured for 7 d during wk 1, 4, and 7 after the dietary change. Feeding competition was measured on d 2, 4, and 6 of each recording week. Sorting activity was determined through particle size analysis of the fresh feed and orts. The particle size separator separated feed into 4 fractions (long, medium, short, and fine). Sorting of each fraction was calculated as actual intake expressed as a percentage of predicted intake. Animals were scored for fecal consistency twice weekly, using a scale from 1 (liquid) to 4 (solid). Heifers were weighed every 2 wk. Neither DM intake (9.0 kg/d) nor average daily gain (1.2 kg/d) differed between treatments. Sorting also did not differ between treatments. Heifers tended to spend more time feeding if they had previously been fed a TDR (198.8 vs. 186.8 min/d). As they had done before the dietary change, heifers previously fed the TDR spent more time at the bunk in the 2 h following feed delivery (40.6 vs. 25.9 min/d). Heifers previously fed the TDR were displaced from the feed bunk more frequently than heifers previously fed the TMR (23.0 vs. 13.1 times/d), particularly during the 2-h period following feed delivery. Fecal scores were lower (more fluid in consistency) for heifers originally fed the TDR (3.2 vs. 3.7). The continued difference in feed bunk competition suggests that heifers previously fed the TDR had learned these patterns in the past, retaining them even when switched to an unfamiliar ration. Furthermore, lower fecal scores for heifers previously fed the TDR suggest altered rumen fermentation, possibly because of altered diurnal consumption patterns. These results show that feeding a TMR to replacement dairy heifers from a young age promotes a more even diurnal feeding pattern, minimizes feed bunk competition, and promotes a more solid fecal consistency.

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 score0.006
Threshold uncertainty score0.013

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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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
Published2013
Admission routes1
Has abstractyes

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