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Record W4323838384 · doi:10.1093/tas/txad028

Supplement feed efficiency of growing beef cattle grazing native<i>Campos</i>grasslands during winter: a collated analysis

2023· article· en· W4323838384 on OpenAlexaff
Fiorella Cazzuli, Javier Sánchez, Andrés Hirigoyen, Pablo Rovira, Virginia Beretta, Álvaro Simeone, Martín Jaurena, Martí­n Durante, Jean Víctor Savian, Dennis Poppi, F. Montossi, Ximena Lagomarsino, Santiago Luzardo, G. Brito, J. Velazco, Carolina Bremm, Fernando Alfredo Lattanzi

Bibliographic record

VenueTranslational Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversity of Prince Edward Island
FundersInstituto Nacional de Investigacion Agropecuaria, UruguayUniversidad de la República UruguayInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria
KeywordsGrazingAnimal scienceBeef cattleDry matterGrasslandBiologySubtropicsPastureAgronomyBody weightEcology

Abstract

fetched live from OpenAlex

Abstract Supplementing growing cattle grazing native subtropical Campos grasslands during winter improves the low, even negative, average daily weight gain (ADG) typical of extensive animal production systems in Uruguay. Nonetheless, to render the practice profitable, it is crucial to control supplement feed efficiency (SFE), that is, the difference in ADG between supplemented and control animals (ADGchng) per unit of supplement dry matter (DM) intake. Little has been studied specifically on how SFE varies in these systems. The objective of this study was to quantify the magnitude and variation in SFE of growing beef cattle grazing stockpiled native Campos grasslands during winter and assess putative associations with herbage, animals, supplements, and climatic variables. We compiled data from supplementation trials carried out in Uruguay between 1993 and 2018, each evaluating between one and six supplementation treatments. The average ADG of unsupplemented and supplemented animals were 0.13 ± 0.174 and 0.49 ± 0.220 kg/animal/day, respectively. In both cases, ADG decreased linearly as the proportion of green herbage in the grazed grassland was lower, but the ADG of unsupplemented animals was further reduced when winter frosts were numerous. Estimated SFE were moderately high, with an average of 0.21 ± 0.076 ADGchng/kg DM, resulting from average ADGchng of 0.38 ± 0.180 kg/animal/day in response to an average supplementation rate of 1.84 ± 0.68 kg supplement DM intake/animal/day (0.86% ± 0.27% body weight). No association was found between SFE and supplementation rate or type (protein vs. energy-based; P > 0.05), but forage allowance negatively affected it, and herbage mass positively affected it, yet in a smaller magnitude, suggesting that a balance is needed between the two to maximize SFE. Weather conditions during trials affected SFE (P < 0.05), with greater SFE in winters with lower temperatures and more frosts. Daytime grazing time was consistently lower in supplemented animals compared to their unsupplemented counterparts, whereas ruminating time during the day was similar, increasing as the proportion of green herbage decreased. Herbage intake estimated from energy balance suggested the existence of some substitution effect. This agrees with the moderately high SFE and with the total digestible nutrients-to-protein ratio of these subtropical humid grasslands being higher than in semi-arid rangelands and dry-season tropical pastures but lower than in sown pastures.

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)0.001
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.241
Teacher spread0.228 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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