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Record W2047852909 · doi:10.2134/agronj14.0321

Influence of Long‐Term Application of Manure Type and Bedding on Yield, Protein, Fiber, and Energy Value of Irrigated Feed Barley

2014· article· en· W2047852909 on OpenAlexafffund
J.J. Miller, Bruce Beasley, C. F. Drury, Francis J. Larney, Xiying Hao

Bibliographic record

VenueAgronomy Journal · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Lethbridge
FundersAgriculture and Agri-Food Canada
KeywordsManureLoamFeedlotAgronomyStrawAnimal scienceHordeum vulgareDry matterFertilizerBeddingNeutral Detergent FiberChemistryPoaceaeEnvironmental scienceBiologyHorticultureSoil waterSoil science

Abstract

fetched live from OpenAlex

The long‐term effect of land application of manure type (composted [CM] vs. stockpiled [SM] manure), bedding (wood‐chips [WD] vs. straw [ST]), and application rate on yield, protein, fiber, and energy value of feed barley ( Hordeum vulgare L.) for beef cattle ( Bos taurus ) is unknown. Dry matter yield and feed quality of irrigated feed barley was measured on a clay loam soil after 5 (2002), 8 (2005), and 12 yr (2009) of annual applications of CM or SM feedlot manure with WD or ST bedding at three application rates (13, 39, 77 Mg ha −1 dry wt.). The treatments also included an unamended control and inorganic fertilizer treatment. Mean yields in 2005 were significantly ( P ≤ 0.05) greater for CM–ST than SM–WD but CM–ST was similar to SM–ST and CM–WD. Yields in 2009 were significantly lower for CM–WD compared to the other three treatments, and were greater for ST‐13 and WD‐77 compared to WD‐13. Crude protein was greater for SM than CM when averaged over the 3 yr. Bedding influenced crude and soluble protein but the effects depended on interactions with rate and year. For example, crude and soluble protein in 2009 was greater for ST than WD at the 39 and 77 Mg ha −1 rates. Manure type and bedding may be practices to manage feed barley yield and protein, but will likely have little or no effect on the fiber content and energy value of the feed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.130

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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 teacher head, 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

Citations8
Published2014
Admission routes2
Has abstractyes

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