MétaCan
Menu
Back to cohort
Record W3113011564 · doi:10.3168/jds.2020-19428

Effect of kernel processing and particle size of whole-plant corn silage with vitreous endosperm on dairy cow performance

2020· article· en· W3113011564 on OpenAlexaff
G.G.S. Salvati, Willian Pereira dos Santos, J.M. Silveira, Viviane Carnaval Gritti, B.A.V. Arthur, Pedro Augusto Ribeiro Salvo, Leticia Castelione Fachin, A.P. Ribeiro, Nilson Nunes Morais Júnior, L.F. Ferraretto, João Luiz Pratti Daniel, K. A. Beauchemin, Flávio Augusto Portela Santos, Luiz Gustavo Nussio

Bibliographic record

VenueJournal of Dairy Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsSilageDry matterSoybean mealEndospermStarchNeutral Detergent FiberFood scienceChemistryForageDairy cattleAgronomyAnimal scienceBiologyRaw materialBiochemistry

Abstract

fetched live from OpenAlex

Kernel processing and theoretical length of cut (TLOC) of whole-plant corn silage (WPCS) can affect feed intake, digestibility, and performance of dairy cows. The objective of this study was to evaluate for lactating dairy cows the effects of kernel processing and TLOC of WPCS with vitreous endosperm. The treatments were a pull-type forage harvester without kernel processor set for a 6-mm TLOC (PT6) and a self-propelled forage harvester with kernel processor set for a 6-mm TLOC (SP6), 12-mm TLOC (SP12), and 18-mm TLOC (SP18). Processing scores of the WPCS were 32.1% (PT6), 53.9% (SP6), 49.0% (SP12), and 40.1% (SP18). Twenty-four Holstein cows (139 ± 63 d in milk) were blocked and assigned to six 4 × 4 Latin squares with 24-d periods (18 d of adaptation). Diets were formulated to contain 48.5% WPCS, 15.5% citrus pulp, 15.0% dry ground corn, 9.5% soybean meal, 6.8% low rumen degradability soybean meal, 1.8% calcium soap of palm fatty acids (FA), 1.7% mineral and vitamin mix, and 1% urea (dry matter basis). Nutrient composition of the diets (% of dry matter) was 16.5% crude protein, 28.9% neutral detergent fiber, and 25.4% starch. Three orthogonal contrasts were used to compare treatments: effect of kernel processing (PT6 vs. SP6) and effect of TLOC (particle size; SP6 vs. SP12 and SP12 vs. SP18). Cows fed SP6 produced 1.2 kg/d greater milk yield with no changes in dry matter intake, resulting in greater feed efficiency compared with PT6. Cows fed SP6 also produced more milk protein (+36 g/d), lactose (+61 g/d), and total solids (+94 g/d) than cows fed PT6. The mechanism for increased yield of milk and milk components involved greater kernel fragmentation, starch digestibility, and glucose availability for lactose synthesis by the mammary gland. However, cows fed SP6 had lower chewing time and tended to have greater levels of serum amyloid A compared with PT6. Milk yield was similar for SP6 and SP12, but SP12 cows tended to have less serum amyloid A with greater chewing time. Cows fed SP18 had lower total-tract starch digestibility and tended to have lower plasma glucose and produce less milk compared with cows fed SP12. Compared with PT6, feeding SP6 raised linear odd-chain FA concentration in milk. Similarly, a reduction of these same FA occurred for SP12 compared with SP6. Cows fed SP6 had greater proportion of milk C14:1 and C16:1 compared with PT6 and SP12. Lesser trans C18:1 followed by greater C18:0 concentrations were observed for SP12 and PT6 compared with SP6, which is an indication of more complete biohydrogenation in the rumen. Under the conditions of this study, the use of a self-propelled forage harvester with kernel processing set for a 12-mm TLOC is recommended for WPCS from hybrids with vitreous endosperm.

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 score0.003

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 communication0.0000.000
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 head0.015
GPT teacher head0.226
Teacher spread0.211 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Dairy ScienceSame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207