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Record W3011097399 · doi:10.3168/jds.2019-17606

Feeding diets varying in forage proportion and particle length to lactating dairy cows: I. Effects on ruminal pH and fermentation, microbial protein synthesis, digestibility, and milk production

2020· article· en· W3011097399 on OpenAlexafffund
Chun Li, K. A. Beauchemin, Wenzhu Yang

Bibliographic record

VenueJournal of Dairy Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaDairy Farmers of Canada
KeywordsSilageDry matterLatin squareNeutral Detergent FiberTotal mixed rationDigestion (alchemy)ForageFood scienceLactoseChemistryAnimal scienceDairy cattleFermentationBiologyRumenLactationAgronomyIce calvingChromatography

Abstract

fetched live from OpenAlex

Physically effective neutral detergent fiber (peNDF) content of dairy cow diets was modified by varying the theoretical chop length of alfalfa silage and forage:concentrate (F:C) ratio, and effects on nutrient intakes, ruminal fermentation, site and extent of digestion, microbial protein synthesis, and milk production were evaluated. Estimates of dietary peNDF contents were compared with recommendations, and predictions of ruminal pH from peNDF and the recently developed physically adjusted neutral detergent fiber (paNDF) system were compared with observed pH. The experiment was designed as a triple 4 × 4 Latin square using 12 mid-lactating dairy cows with 4 intact, 4 ruminally cannulated, and 4 ruminally and duodenally cannulated cows. Site and extent of digestion and microbial protein synthesis were measured in a single 4 × 4 Latin square. Treatments were a 2 × 2 factorial arrangement; 2 forage particle lengths (FPL) of alfalfa silage (short and long) were combined with low (35:65) and high (60:40) F:C ratios [dry matter (DM) basis]. The peNDF contents were determined by multiplying the proportion (DM basis) of total mixed ration retained on 2 (8 and 19 mm; peNDF 8.0 ) or 3 (1.18, 8, and 19 mm; peNDF 1.18 ) sieves of the Penn State Particle Separator by the neutral detergent fiber content of the diet. The dietary peNDF contents ranged from 10.7 to 17.5% for peNDF 8.0 or from 23.1 to 28.2% for peNDF 1.18 . Interactions between F:C ratio and FPL content were few. Increasing peNDF content of diets by increasing F:C ratio decreased DM intake, milk yield, and milk protein yield, whereas apparent total-tract DM digestibility and milk efficiency improved. Increasing F:C ratio improved ruminal pH status but decreased total volatile fatty acid concentration and microbial protein synthesis. Increasing peNDF content of diets via dietary FPL increased mean ruminal pH, but did not affect DM intake, total-tract digestibility, or milk production. The results indicate that feeding dairy cows a low F:C diet helps increase DM intake, milk production, and microbial protein synthesis, but may adversely affect feed digestibility and milk efficiency due to increased risk of subacute ruminal acidosis. Increased FPL improved ruminal pH status, but had minimal effects on feed intake, ruminal fermentation, nutrient digestibility, and milk production. The results indicate a trade-off between reducing the risk of subacute ruminal acidosis and maximizing ruminal fermentation, feed digestibility, and milk production of dairy cows. The paNDF model showed improvement in the predictability of ruminal pH over the peNDF model, but the accuracy of predictions varied depending upon the diet and ruminal fermentation variables considered in the equations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.026
GPT teacher head0.257
Teacher spread0.231 · 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 designBench or experimental
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

Citations38
Published2020
Admission routes2
Has abstractyes

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