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Record W4296619076 · doi:10.1093/jas/skac247.165

187 Body Performance of Beef Steers Swathgrazing oat Monoculture or Forage-Blends During Winter Months

2022· article· en· W4296619076 on OpenAlexaff
Obioha N Durunna, Carien Vandenberg, Deanna Krys, Bart Lardner

Bibliographic record

VenueJournal of Animal Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMonocultureForageHectareBeef cattleGrazingAnimal scienceBiologyCanolaAgronomyFeedlotCrossbreedBody weightBreed

Abstract

fetched live from OpenAlex

Abstract Extended grazing strategies have the potential to reduce beef winter-feeding costs. Beef producers can incorporate monoculture cereal crops in their winter swathgrazing programs, but recently, the use of polycrops (also known as intercrops or cover crops) is growing. There is little information about beef cattle body performance when swathgrazing forage blends during winter months. This study evaluated animal performance when swathgrazing oat monoculture or a polycrop blend (oat, turnip,canola and forage pea). Forage systems (treatment) were seeded on a 12-hectare site, further separated into 2-hectare paddocks. The paddocks were seeded in June and swathed in August at the hard dough stage for the oats. Forty-two crossbred steers were randomly allocated to one of two replicated (n=3) systems while balancing for body weight and breed composition. Body weights (BW) were determined over two consecutive days at the start and end of grazing, and at 2-wk intervals throughout the 86 d grazing period. Ultrasound backfat, rumpfat and intramuscular fat were collected at the start and end of the trial. At the start of the trial, two steers in each replicate group were fitted with rumen pH/temperature boluses. The biomass yield of the oat and polycrop were 6,225±651 and 4,867±471 kg DM ha-1, respectively, while mean regrowth for oat and polycrop were 177 and 1409 kg DM ha-1, respectively. Start-of-test BW (246, 247kg; P = 0.92), end-of-test BW (304, 303.5; P = 0.97), average daily gain (0.68, 0.74 kg d-1; P = 0.61), backfat change (0.53, 0.69 mm; P =0.65), rumpfat change (0.84,1.30 mm; P=0.14) and intramuscular fat change (-0.12, 0.14%; P=0.66) did not differ for steers assigned to oat and polycrop systems, respectively. Other measures were mean rumen temperatures (39.13oC, 38.96oC; P=0.03) and pH (6.76, 6.70; P=0.10) for the oat and polycrop respectively. These results suggest that polycrops may be an alternative in a swath grazing system during the winter months.

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 score0.003
Threshold uncertainty score0.005

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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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
Published2022
Admission routes1
Has abstractyes

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