MétaCan
Menu
Back to cohort
Record W2554901037 · doi:10.2527/jam2016-1241

1241 Accuracy and precision of diets for high-producing dairy cows and their impacts on production and milk composition

2016· article· en· W2554901037 on OpenAlexaff
Jorge Henrique Carneiro, Junio Fabiano dos Santos, Patrick Schmidt, T.J. DeVries, Rodrigo de Almeida

Bibliographic record

VenueJournal of Animal Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHayAnimal scienceForageComposition (language)Milk productionHerdTotal mixed rationMilk fatFood scienceLactoseBiologyLactationChemistryIce calvingAgronomy

Abstract

fetched live from OpenAlex

The goal of this study was evaluate associated feeding management and nutritional accuracy with milk production and composition on commercial herds. Twenty high-producing dairy farms from Campos Gerais county, Paraná State, Southern Brazil, were visited for 3 consecutive days in the 2015 fall season. Feeding management and TMR preparation related variables, and the physical and chemical characteristics of the offered diets and orts were collected. Production performance and milk composition from the high-production group of cows were obtained from regular milk testing, performed on average 1 ± 5d before or after the data collection period. Pearson correlations were estimated among the management and diet variables with production, milk composition, and feed sorting estimates. Using the Penn State Particle Separator, the offered diets had on average 14.9, 41.8, 32.8, and 10.5% (DM) of long, medium, short, and fine particles, respectively. Long particles showed a daily refusal rate of 9.0%, whereas short and medium particles were preferentially consumed at 1.1 and 1.7%, respectively. A high proportion of long particles in the forage (78.2% of haylage and hay) was associated with reduction in milk fat % (%MF) (r = −0.50; P < 0.05), and an increased proportion of cows with fat:protein ratio lower than 1 (FPR < 1) (r = 0.50; P < 0.05). Errors associated with loading an excess of concentrate ingredients in the TMR wagon were negatively associated with %MF (r = −0.52; P = 0.05) and milk production (r = −0.47; P < 0.05). By comparing the formulated diet with the one delivered to the cows, we noted, on a DM basis, a decrease in CP (−3.1%), fat (−7.0%), and ash contents (−10.5%), and an increase in NDF (+10.3%). The accuracy observed between formulated and delivered diets was not associated with the performance of the cows. However, daily variation of the DM content of the diet was associated with a greater proportion of cows with FPR < 1, and reduced FPR (r = 0.40; P = 0.09 and r = −0.43; P = 0.07, respectively). Low homogeneity (across 3 d) of the % of long particles in the diet was associated with greater selection against these particles (r = −0.64; P < 0.05), which showed a curvilinear association with %MF. These results demonstrated that the addition of more concentrate ingredients than expected, as well as the inconsistent intake of different particle sizes throughout the day, had a negative impact on milk production and composition of the studied herds.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Animal ScienceSame topicEffects of Environmental Stressors on LivestockFrench-language works237,207