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Record W2169524573 · doi:10.4141/a04-060

Feedlot cattle diets based on barley or corn supplemented with dry corn gluten feed evaluated using the NRC and CNCPS beef models

2005· article· en· W2169524573 on OpenAlexaffvenueabout
K. A. Beauchemin, K. M. Koenig

Bibliographic record

VenueCanadian Journal of Animal Science · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsFeedlotSilageBeef cattleGlutenAnimal scienceCrossbreedBiologyAgronomyAnimal feedFood science

Abstract

fetched live from OpenAlex

A study was conducted to evaluate the effects of supplementing feedlot finishing corn diets with dry corn gluten feed (CGF). Animal performance and carcass traits were compared with those obtained by feeding a barley-based diet typical of diets fed commercially in western Canadian feedlots. The second objective of the experiment was to evaluate the diets using the 1996 National Research Council (NRC) Beef (level 1 and 2) and Cornell Net Carbohydrate and Protein System (CNCPS, version 5.0) models to determine how well the models predict animal response. Crossbred beef steers (248) weighing 423 ± 20.5 kg were allocated to 24 pens and then six pens were assigned to each of four diets: (1) barley grain (13.8% crude protein, CP), (2) corn grain (9.2% CP), (3) corn grain supplemented with a low level (7%, DM basis) of CGF (CGF-low, 9.7% CP), and (4) corn grain supplemented with a high level (23%) of CGF (CGF-high, 11.7% CP). The barley grain was steam-rolled and the corn grain was dry-rolled. All diets contained 8% barley silage (DM basis). The cattle were adapted from a backgrounding diet to the experimental diets over a period of 28 d and then fed for a period of 136 d. During the adaptation phase cattle fed unsupplemented corn grew slower (P < 0.05) than the cattle fed the other diets (1.23 vs. 1.52 kg d -1 ). However, after day 42 of the study, cattle fed unsupplemented corn grew very rapidly such that gain for the entire feeding period was similar for cattle fed unsupplemented corn and barley diets (1.62 vs. 1.58, P > 0.05). Furthermore, growth rate was higher (P < 0.05) for cattle fed barley or unsupplemented corn than for cattle fed low or high levels of CGF (1.48 and 1.45 kg d -1 , respectively). The dry matter intake (DMI) averaged 11.4 kg d -1 during the feeding period and was not affected (P > 0.05) by diet. Feed:gain ratio (kg DM:kg gain) was similar (P > 0.05) for cattle fed barley (7.09) and unsupplemented corn (7.38), but was higher (P < 0.05) for cattle fed CGF-high (7.92), and intermediate (P > 0.05) for those fed CGF-low (7.52). There were no differences in carcass composition between barley and corn-fed cattle, but those fed low and high levels of CGF had carcasses that were fatter with lower lean meat yield. The three models underestimated DMI of all four diets. When DMI was set to observed intake in the models, CNCPS accurately predicted ADG and F:G ratio for cattle fed corn diets, but all models underestimated ADG of cattle fed barley. All models correctly predicted that gain during the feeding period was not limited by protein supply. The results from this study indicate that rolled-corn diets containing about 9.5% CP, with 50% of the CP as DIP are adequate for feedlot cattle weighing more that 500 kg. Substituting dry CGF for corn grain reduces feed efficiency and gain due to the lower digestible energy content of CGF. Key words: Grain, barley, corn, corn gluten feed, feedlot cattle, protein, models

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.951
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.265
Teacher spread0.208 · 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 teacher head, 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
Published2005
Admission routes3
Has abstractyes

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