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Record W2190464249 · doi:10.3168/jds.2015-9619

Effects of replacing grass silage with forage pearl millet silage on milk yield, nutrient digestion, and ruminal fermentation of lactating dairy cows

2015· article· en· W2190464249 on OpenAlexafffund
T. Brunette, B. Baurhoo, A. F. Mustafa

Bibliographic record

VenueJournal of Dairy Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsBell (Canada)McGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsSilageDry matterLatin squareNeutral Detergent FiberForageNutrientDigestion (alchemy)BiologyAnimal scienceDairy cattleRumenAgronomyFermentationChemistryFood science

Abstract

fetched live from OpenAlex

This study investigated the effects of dietary replacement of grass silage (GS) with forage millet silages that were harvested at 2 stages of maturity [i.e., vegetative stage and dough to ripe seed (mature) stage] on milk production, apparent total-tract digestibility, and ruminal fermentation characteristics of dairy cows. Fifteen lactating Holstein cows were used in a replicated 3 × 3 Latin square experiment and fed (ad libitum) a total mixed ration (60:40 forage:concentrate ratio). Dietary treatments included control (GS), vegetative millet silage (EM), and mature millet silage (MM) diets. Experimental silages comprised 24% of dietary dry matter (DM). Soybean meal and slow-release urea were added in millet diets to balance for crude protein (CP). Three additional ruminally fistulated cows were used to determine the effect of treatments on ruminal fermentation and total-tract nutrient utilization. Cows fed the GS diet consumed more DM (22.9 vs. 21.7 ± 1.02 kg/d) and CP (3.3 vs. 3.1 ± 0.19 kg/d), and similar starch (4.9 ± 0.39 kg/d) and neutral detergent fiber (NDF; 8.0 ± 0.27 kg/d) compared with cows fed the MM diet. Replacing the EM diet with the MM diet did not affect DM, NDF, or CP intakes. Cows fed the MM diet produced less milk (26.1 vs. 29.1 ± 0.79 kg/d), energy-corrected milk (28.0 vs.30.5 ± 0.92 kg/d), and 4% fat-corrected milk (26.5 vs. 28.3 ± 0.92 kg/d) yields than cows fed the GS diet. However, cows fed diets with EM and GS produced similar yields of milk, energy-corrected milk, and 4% fat-corrected milk. Feed efficiency (milk yield:DM intake) was greater only for cows fed the GS diet than those fed the MM diet. Milk protein yield and concentration were greater among cows fed the GS diet compared with those fed the EM or MM diets. Milk fat and lactose concentrations were not influenced by diet. However, milk urea N was lower for cows fed the GS diet than for those fed the MM diet. Ruminal NH3-N was greater for cows fed the EM diet than for those fed the GS diet. Total-tract-digestibility of DM (average = 66.1 ± 3.3%), NDF (average = 55.1 ± 2.4%), CP (average = 63.6 ± 4.2%), and gross energy (average = 64.5 ± 2.6%) were not influenced by experimental diets. We concluded that cows fed GS and EM diets had comparable performance, whereas milk yield was significantly reduced with the MM diet, likely because reduced intakes of DM and net energy for lactation.

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 score0.002
Threshold uncertainty score0.004

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 head0.021
GPT teacher head0.246
Teacher spread0.225 · 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

Citations16
Published2015
Admission routes2
Has abstractyes

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