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Record W4388528829 · doi:10.1093/jas/skad281.632

PSXIV-1 Impact of Slow-Release Nitrogen Or Regular Protein-Based Supplementation During Late Gestation on Maternal and Offspring Performance in Beef Cattle

2023· article· en· W4388528829 on OpenAlexaff
Mateus Pies Gionbelli, Diana Cediel-Devia, Karolina Batista Nascimento, Thaís Correia Costa, Luana Santos, Germán Darío Ramírez-Zamudio, Márcio de Souza Duarte

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAnimal scienceSoybean mealOffspringIce calvingDry matterWeaningBrahmanGestationMorningBeef cattleMealBiologyUreaLactationPregnancyChemistryFood scienceBiochemistryBreedBotany

Abstract

fetched live from OpenAlex

Abstract The objective of this study was to determine whether supplementing pregnant beef cows with regular nitrogen or a slow-release nitrogen source at the late gestation could enhance the use of a low protein basal diet and improve the performance of cows and their offspring until weaning. Thirty-five pregnant Brahman cows (556 ± 47 kg and 4.5 ± 1.4 years old) were individually housed in covered pens and randomly divided into three groups. From 180 ± 19 days of gestation until calving, cows were fed either a control (CON, n = 12) low crude protein (CP) basal diet (6% of CP, ad libitum) plus mineral mixture (130 g·cow-1·d-1), or the CON treatment supplemented with protein concentrate supplements (40% CP, fed 2 g·kg of BW-1·d-1 in the morning) based either on a regular (REG, n = 11) composition (corn, soybean meal, and urea) or on a slow-release N (SRN, n = 12) source (Timafeed Boost, Roullier Group, Saint-Malo, France) which replaced 60% of soybean meal and urea in the supplement composition. The MN, fetal sex (FS), and MN×FS were used as fixed effects in the statistical model, while the number of previous calvings was considered as a random effect. Maternal initial BW was used as a covariate. Statistical differences were declared at P < 0.05. Supplementation with REG and SRN increased total dry matter intake (DMI) and basal diet intake compared with the control treatment (P < 0.01). Protein supplementation allowed the cows to have a higher average daily gain (P < 0.01; 0.177 and 0.193 kg/day, respectively, for REG and SRN) than the CON treatment (-0.255 kg/day). Body condition score (BCS) also increased (P < 0.01) when cows received protein supplementation in late pregnancy, but with no differences between REG and SRN cows (P = 0.31). Interactions between MN and FS were observed for calf birth weight (CBW, P < 0.01) and calves' average daily gain up to 120 days of age (ADG120, P = 0.01), but not at weaning weight (P = 0.69). Male calves born from REG and SRN cows were born heavier and gained more weight up to 120 days after birth than calves born from CON cows (P < 0.05) but did not differ from each other (P = 0.94 and 0.92, respectively, for CBW and ADG120). Female calves had no differences in CBW and ADG120 as a function of maternal dietary treatment during pregnancy. The REG and SRN calves weaned heavier (P < 0.05) than CON calves. In conclusion, supplementing protein to pregnant beef cows during periods of low CP pasture improves maternal and progeny performance. The SRN-based product can replace the major part of soybean and urea in the protein supplement, achieving same level of improvement in forage utilization and animal performance as the regular protein-based supplement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 communication0.0000.000
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 head0.021
GPT teacher head0.275
Teacher spread0.254 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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