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Record W4402541582 · doi:10.1093/jas/skae234.175

517 The effects of pellet starch concentration and allocation amount for Holstein cows in peak, mid, and late lactation in an automatic milking system

2024· article· en· W4402541582 on OpenAlexaff
Sophia Cattleya Dondé, Anna J Schwank, T.J. DeVries, R. Claro Da Silva, G.B. Penner

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversity of GuelphUniversity of Saskatchewan
Fundersnot available
KeywordsMilkingPelletLactationAnimal scienceAutomatic milkingStarchChemistryAgronomyBiologyFood sciencePregnancyIce calvingEcology

Abstract

fetched live from OpenAlex

Abstract This study evaluated the effects of pellet starch concentration and amount of pellet delivered in an automated milking system (AMS) on AMS pellet and partial mixed ration (PMR) intake, milk and milk component yield, and milking and feeding behavior for Holstein cows at different stages of lactation. Twenty-four Holstein cows at peak [n = 8; 85 ± 25.7 days in milk (DIM)], mid (n = 8; 185 ± 35.1 DIM) or late (n = 8; 290 ± 69.5 DIM) lactation (3 squares/DIM category), housed in a free-stall barn with a feed-first guided traffic flow AMS, were used. Treatments included low (LS; 24% DM) or high (HS; 34% DM) starch pellets that were provided at low (LA; 2 kg/d DM) or high (HA; 6 kg/d DM) quantities within replicated 4 × 4 Latin square design. Each period lasted 21 d including 16 d of adaptation and 5 d of data and sample collection to evaluate feed intake and behavior, milking characteristics, milk and milk component yields. Pellet starch did not affect the amount of pellet consumed, but HA cows consumed more pellet than LA (4.3 vs. 1.8 kg/d; P < 0.01). Relative to LA, HA had greater pellet refused in the AMS (P < 0.01) and decreased PMR intake (P = 0.04). Total DMI was 1.3 kg/d greater for HA than LA cows (P = 0.05). Pellet starch and DIM did not affect PMR intake or DMI. Neither pellet starch nor allocation affected the number of PMR meals; however, LA increased PMR eating time by 20 min/d (P < 0.01) and PMR meal length by 2 min/meal (P = 0.03). Milking frequency was not affected by pellet starch or DIM, but HA tended to increase total milking frequency (2.7 vs. 2.6 no/d; P = 0.06) over LA, with greater voluntary milkings (2.5 vs 2.3 no/d; P < 0.01). Milk yield/visit and milking duration were not affected by pellet starch or allocation. Milk yield was not affected by pellet starch, or the amount allocated averaging 43.2, 41.3 and 43.2 kg/d for peak, mid, and late lactation cows (P ≥ 0.19). Compared with LS, HS decreased milk fat concentration (4.1 vs 3.9%; P < 0.01) and providing the HA reduced milk fat concentration (4.1 vs 3.96%; P < 0.01) when compared with LA; however, neither pellet starch nor allocation affected fat yield, averaging 1.67 kg/d (P ≥ 0.15). True protein yield was not affected. Cows fed the LA had greater milk urea nitrogen (14.8 mg/dL) relative to HA (14.3 mg/dL; P = 0.03). Increasing the amount of pellet allocated in the AMS reduced PMR intake, while increasing total DMI and resulted in greater amounts of pellet refusals by cows; however, greater amounts of pellet may increase attendance at the AMS without affecting milk or milk component yields. Starch concentration of the pellet had little effect on productivity.

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)0.000
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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
GPT teacher head0.278
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
Published2024
Admission routes1
Has abstractyes

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