MétaCan
Menu
Back to cohort
Record W4402541656 · doi:10.1093/jas/skae234.720

PSLBI-24 Optimizing crop byproduct inclusion in beef cattle diets: Utilization of wheat straw to improve operational economics and oilseed screenings as protein supplements

2024· article· en· W4402541656 on OpenAlexaffabout
Beatriz J Montenegro, G.B. Penner, H.A. Lardner, Kathy Larson, J. J. McKinnon, D. J. Gibb, Tim A. McAllister, Gabriel O Ribeiro

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsStrawCropAgronomyBeef cattleBiologyBiotechnologyAnimal science

Abstract

fetched live from OpenAlex

Abstract Inclusion of crop residues and byproducts in beef cattle diets has become a significant practice in beef cattle production as producers are faced with conventional feed shortages, increased prices, and disrupted supply chains. Wheat straw is a widely available crop byproduct in the western Canadian prairies whose inclusion in beef cattle diets has been prevented by its low nutritional content, necessitating supplementation strategies to enhance its nutritional value. The present study explored the effect of incorporating wheat straw in backgrounding cattle diets with canola or flax screening supplementation to improve the nutritional profile of the diet and determine the sustainability of such inclusion through cattle performance measurements. The experiment was a completely randomized design. Steers [n = 300; initial body weight (BW): 297 ± 18 kg] were randomly assigned to 5 treatment diets, each diet having 4 pen replicates (15 steers/pen); control (CTL), low straw canola (LSC), low straw flax (LSF), high straw canola (HSC) and high straw flax (HSF). The CTL diet was a conventional Western Canadian backgrounding diet (60% barley silage:40% dry rolled barley grain-based concentrate). Low straw diets had a 25% wheat straw inclusion and high straw diets had 50% wheat straw inclusion on a dry matter (DM) basis. Respective screenings were included at 12.50% inclusion of diet DM. The steers were fed for a total of 84 d divided into four periods of 21 d. Statistical analyses were performed using the MIXED procedure of SAS 9.4 with diet treatment included as a fixed effect and pen within diet as a random effect. Treatment means were compared using the LSMEANS statement adjusted for the Tukey-Kramer method. Final BW, total BW gain, average daily gain (ADG) and gain:feed were greater (P < 0.001) for the CTL steers (Table 1). Dry matter intake (DMI) was greatest for CTL steers and decreased with increasing straw inclusion in the diet (P < 0.001). The type of screenings (canola or flax) did not affect (P > 0.05) steers final BW, total BW gain, ADG and gain:feed. Increasing the inclusion of wheat straw in backgrounding diets decreased growth performance parameters. The high level of neutral digestible fiber in wheat straw limited DMI and reduced growth performance compared with CTL steers. The addition of screenings to the diet provided protein content to offset the low nutritional value of the wheat straw; however, the different types of screenings did not result in a cattle performance advantage over the other. Although these findings demonstrate the challenges of including low nutritional crop byproducts, further research is needed to analyze the effects different diets have on operational economics, rumen fermentation parameters and greenhouse gas emissions to further outline any advantages or disadvantages.

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)0.000
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.028
GPT teacher head0.283
Teacher spread0.255 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Animal ScienceSame topicAnimal Nutrition and PhysiologyFrench-language works237,207