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Record W2766128743 · doi:10.3168/jds.2017-12930

Mitigation of variability between competitively fed dairy cows through increased feed delivery frequency

2017· article· en· W2766128743 on OpenAlexafffund
R.E. Crossley, Alexandra Harlander-Matauschek, T.J. DeVries

Bibliographic record

VenueJournal of Dairy Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLactationAnimal scienceTotal mixed rationRuminationDry matterDairy cattleCrossover studyMealBiologyMedicineFood sciencePregnancyIce calvingPlacebo

Abstract

fetched live from OpenAlex

The objective of this study was to determine whether increased frequency of total mixed ration (TMR) delivery could mitigate the effects of feed bunk competition on the behavior and productivity of individual lactating dairy cows within a group. We hypothesized that, for competitively fed cows, a greater frequency of TMR delivery would improve access to feed, and reduce individual variability in behavior, meal patterns, and production between cows. Sixteen lactating Holstein dairy cows (first lactation = 4, second lactation = 5, ≥ third lactation = 7) averaging 72 ± 35 d in milk and producing 42 ± 6 kg of milk/d at the start of the trial, were categorized as either young (≤ second lactation) or mature (≥ third lactation) and paired to maximize difference in parity. Pairs were housed 4 at a time and competitively fed a TMR at a ratio of 2 cows:1 feed bin. Cow pairs were exposed, in a crossover design, to each of 2 feed delivery frequency treatments: low (2×/d) and high (6×/d) frequency. Treatments were applied for 10 d, with dry matter intake (DMI), feeding behavior (feeding time, feeding rate, and meal patterns), and replacement frequency for each cow recorded using an automated feed intake system on d 6 to 10 of each period. Rumination time, feed sorting, lying behavior, and productivity were also measured for this period. Variability in behavior within pairs of cows was determined by averaging the absolute difference within each pair over the recording period to provide 1 value per pair. Frequency of TMR delivery did not affect feeding time, feeding rate, DMI, replacement frequency, feed sorting, or productivity. At the high delivery frequency, there was a tendency for rumination time to increase [low = 519.3; high = 544.3 min/d; standard error of the difference (SED) = 11.32], and to be more variable within pairs (low = 38.0, high = 50.0 min/d; SED = 5.57). Cows also had longer lying bouts at the high delivery frequency (low = 53.0; high = 55.5 min/bout; SED = 1.00). No differences in daily meal patterns were found between treatments; however, the average first meal following each feeding indicated that cows under the high delivery frequency spent less time, consuming smaller meals during peak feeding periods. Comparing the young and mature individuals within each treatment pair revealed that feeding rate (young = 0.16; mature = 0.19 kg/min; SED = 0.014) and DMI (young = 25.6; mature = 28.6 kg of DM/d; SED = 1.36) were lower for the young cows on both treatments. Meal frequency was greater in young cows (young = 9.0; mature = 7.5 meals/d; SED = 0.71) and meal size was greater in mature cows (young = 3.2; mature = 4.2 kg of DM/meal; SED = 0.32) across treatments. These results suggest that for cows fed at a high level of competition, increasing TMR delivery frequency from 2 to 6×/d led to consumption of shorter, smaller meals during peak periods of feed consumption. However, under these conditions, the relative parity of competitively fed cows had a greater effect on feeding behavior, meal patterns, and production than did the frequency of feed delivery.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.001
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 head0.035
GPT teacher head0.273
Teacher spread0.238 · 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

Citations28
Published2017
Admission routes2
Has abstractyes

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