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Record W2970691908 · doi:10.3168/jds.2019-16756

Effects of concentrate location on the behavior and production of dairy cows milked in a free-traffic automated milking system

2019· article· en· W2970691908 on OpenAlexafffund
A.J. Schwanke, K.M. Dancy, T. Didry, G.B. Penner, T.J. DeVries

Bibliographic record

VenueJournal of Dairy Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversity of SaskatchewanUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Guelph
KeywordsMilkingAnimal scienceDry matterAutomatic milkingCrossover studyMilk productionChemistryLactationBiologyMedicinePregnancyIce calving

Abstract

fetched live from OpenAlex

The objective of this study was to determine whether the amount of concentrate allowance in an automated milking system (AMS) affects partial mixed ration (PMR) sorting behavior, milking activity, and production of lactating dairy cows fed isocaloric diets. Fifteen primiparous Holstein cows were used in a crossover design with 28-d periods, including 14 d of adaptation and 14 d of data collection. The cows were housed in a freestall pen with free-traffic access to the AMS. Treatments consisted of a higher-concentrate PMR (H-PMR) with a pelleted concentrate allowance of 3.0 kg/d on a dry matter (DM) basis in the AMS, or a lower-concentrate PMR (L-PMR) with a pelleted concentrate allowance of 6.0 kg/d in the AMS. As designed, cows on the L-PMR had greater AMS concentrate intake (6.3 vs. 3.1 kg/d of DM) compared with the H-PMR. The standard deviation for mean concentrate intake among days increased from 0.38 to 1.0 kg/d with greater targeted AMS concentrate intake. When fed the L-PMR diet, PMR intake was reduced compared with when cows were fed the H-PMR diet (17.1 vs. 19.1 kg/d of DM). The reduction in PMR intake was compensated for by greater AMS concentrate intake; thus, cows on the L-PMR had greater total dry matter intake (DMI; 23.6 vs. 22.3 kg/d). Cows sorted against long (>19 mm) and fine (<4 mm) PMR particle fractions, and in favor of medium (8 to 19 mm) and short (4 to 8 mm) PMR fractions when on the H-PMR treatment, but only sorted against the medium and in favor of the short PMR fractions on the L-PMR treatment. PMR eating rate and total time spent eating PMR did not differ significantly between the 2 treatments; however, meal size tended to be larger when cows were fed the H-PMR compared with the L-PMR (2.2 vs. 2.1 kg DM/meal). Cows tended to spend 30.8 min/d more time lying down when fed the L-PMR. On the L-PMR treatment, cows tended to have more voluntary AMS visits (5.9 vs. 4.6 visits/d), were fetched less (0.1 vs. 0.5 times daily), and had a greater milking frequency (3.5 vs. 3.0 milkings/d) compared with when they were on the H-PMR treatment. However, milk yield was not affected by treatment. These data suggest that allocating a greater proportion of total dietary concentrate to the AMS, in a free-traffic setup, may improve milking activity and decrease the need for fetching, as well as promoting greater amounts of and maintaining consistency in total dry matter consumption.

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 score0.003
Threshold uncertainty score0.006

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 head0.008
GPT teacher head0.216
Teacher spread0.207 · 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

Citations17
Published2019
Admission routes2
Has abstractyes

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