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Record W2891573464 · doi:10.3168/jds.2018-14665

The effect of the forage-to-concentrate ratio of the partial mixed ration and the quantity of concentrate in an automatic milking system for lactating Holstein cows

2018· article· en· W2891573464 on OpenAlexafffund
Silvia Beatriz Menajovsky, C.E. Walpole, T.J. DeVries, K. S. Schwartzkopf-Genswein, M. E. Walpole, G.B. Penner

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of GuelphUniversity of Saskatchewan
FundersDairy Farmers of ManitobaAlberta Livestock and Meat AgencyMinistry of Agriculture - Saskatchewan
KeywordsMilkingDry matterForageLatin squareAnimal scienceTotal mixed rationBarnAutomatic milkingNeutral Detergent FiberChemistryRumenMathematicsBiologyFermentationFood scienceLactationAgronomy

Abstract

fetched live from OpenAlex

This study was conducted to evaluate the effects of the forage-to-concentrate ratio of the partial mixed ration (PMR) and the quantity of concentrate offered in an automated milking system (AMS), in a feed-first guided-flow barn, on the behavior and performance of dairy cows. Eight ruminally cannulated multiparous Holstein cows were used in a replicated 4 × 4 Latin square balanced for carry-over effects. Treatments were arranged in a 2 × 2 factorial consisting of a PMR that contained (dry matter basis) either a low (54:46; L-FOR) or a high (64:36; H-FOR) forage-to-concentrate ratio and AMS concentrate provision to achieve low (2 kg/d; L-AMS) or high (6 kg/d; H-AMS) intake. Each period consisted of 28 d with 6 d for dietary transition, 13 d for adaptation, and 9 d of collection. The first 4 d of data and sample collection were used to evaluate behavioral data (milking frequency, feeding behavior, and standing and lying behavior) and ruminal pH. Subsequently, a sampling device removal day was provided, and the last 4 d were used to evaluate ruminal fermentation and apparent total-tract digestibility. All 9 d were used for milk yield measurement, and the 8 d were used for dry matter intake measurement. Cows fed the H-AMS consumed 3.5 kg/d less PMR while consuming 4.2 kg/d more AMS concentrate, but total dry matter intake (PMR+AMS) was not affected by treatments averaging 27.3 kg/d. Although cows fed H-AMS had greater concentrate intake, they also had greater variability for AMS concentrate intake among days (0.85 vs. 0.25 kg/d, respectively). The number of PMR meals and PMR eating behavior were not affected by the PMR or AMS treatments. Feeding H-AMS did not affect milking frequency averaging 3.63 milkings/d, but tended to increase milk yield by 1.25 kg/d relative to L-AMS. Likewise, cows fed the L-FOR tended to have greater milk yield relative to H-FOR (39.3 vs 37.9 kg/d, respectively), but had greater holding area time. Minimum ruminal pH tended to be lower for cows fed L-FOR compared with cows fed H-FOR but was not affected by the AMS concentrate treatment. When fed the L-FOR, feeding the H-AMS increased total short-chain fatty acid concentration in the rumen relative to cows fed L-AMS, whereas the response for H-FOR was not affected by the AMS concentrate. These data suggest that feeding H-AMS may improve milk yield, but also increases the day-to-day variability in AMS concentrate consumption. Feeding a L-FOR PMR may increase milk yield without affecting variability in AMS concentrate consumption; however, it may reduce ruminal pH and increase the time spent in the holding area compared with cows fed a H-FOR PMR.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0010.000
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 head0.011
GPT teacher head0.246
Teacher spread0.234 · 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

Citations30
Published2018
Admission routes2
Has abstractyes

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