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Record W4402533605 · doi:10.1093/jas/skae234.655

PSVIII-17 A comparison of a monoculture barley crop to a barley-based complex mixture on forage yield, quality, dry matter intake, enteric methane emissions and growth performance of pregnant yearling heifers

2024· article· en· W4402533605 on OpenAlexaffabout
Brittany Schreiner, Gabriel O Ribeiro, H.A. Lardner, G.B. Penner

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDry matterAgronomyForageMonocultureBiologyCropYield (engineering)SorghumPhysics

Abstract

fetched live from OpenAlex

Abstract The objective of the study was to compare forage yield and composition, dry matter intake (DMI), growth, and enteric methane emissions for heifers grazing a barley monoculture (MON) or a barley-based complex forage blend (COM). The 2-year study was conducted near Saint Brieux (SK, Canada) and evaluated 2 treatments with 4 paddocksּ treatment-1ּ yr-1. Treatments included MON (CDC Rosser) seeded at 123 kg/ha or a COM which contained barley seeded at 61.5 kg/ha and a mixture of species seeded at 12.3 kg/ha. The COM mixture included 54% millet (Proso and Japanese), 14% Italian ryegrass, 13.5% clover (Berseem, Persian, Crimson), 4.5% hairy vetch, 4.5% black oil sunflower, 3% forage collards, 3% turnips, 2% phacelia, and 1% chicory. Forages were seeded in alternating strips (0.4 ha) to create individual paddocks. Pregnant yearling heifers (n = 80/yr) were weighed on 2 consecutive days at the start and end of the study, randomly allocated based on BW to each treatment, and assigned to a paddock (10 heifers/paddock). Heifers were provided fresh forage daily. Grazing was initiated at the late milk stage for MON and continued until the hard dough stage of maturity. Paddocks were then swathed, and grazing continued resulting in 84 and 77 d in yr 1 and 2, respectively. Forage samples and residual forage were collected weekly to estimate forage yield, composition, and intake. Rib and rump fat measurements were taken at the start and end of the study. Methane emissions were measured using the sulfur hexafluoride (SF6) tracer gas technique over a 4-d period while grazing both standing and swathed crops in each year. Forage yield [dry matter (DM) basis] did not differ among treatments (P = 0.59) during the growing season averaging 5.6 t/ha and did not differ (P = 0.57) once swathed averaging 5.7 t/ha. Treatment and date of sampling affected (P < 0.01) the proportion of barley, with barley representing 95% of the DM for MON and 81% for COM. Chemical composition did not differ among treatments for OM, CP, NDF, lignin, and starch; however, DM tended (P = 0.08) to be less for COM, and ADF and WSC were greater (P = 0.01 and P = 0.02) for COM than MON. Dry matter intake (DMI) was not affected while grazing standing (P = 0.98) or swathed (P = 0.50) forages with mean values of 8.8 and 10.2 kg/d, respectively. Rib and rump fat did not differ, but average daily gain was 0.1 kg/d greater (P < 0.01) for COM than MON. As a result, gain:feed was 0.08 kg/kg greater for heifers grazing COM (P < 0.01) than MON. Enteric methane emissions did not differ for treatment when heifers were grazing standing (P = 0.77) or swathed (P ≥ 0.99) forages. Growing an annual complex forage mixture rather than a monoculture crop may improve growth of yearling heifers, but does not affect forage yield, DMI, or enteric methane production.

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 score0.007
Threshold uncertainty score0.014

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 head0.062
GPT teacher head0.323
Teacher spread0.261 · 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".

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Citations1
Published2024
Admission routes2
Has abstractyes

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