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Record W2135694446 · doi:10.2174/1874331500802010068

Forage Yield and Quality of Chicory, Birdsfoot Trefoil, and Alfalfa During the Establishment Year

2008· article· en· W2135694446 on OpenAlexafffundabout
Grant Chapman, Edward W. Bork, Noble T. Donkor, Robert J. Hudson

Bibliographic record

VenueThe Open Agriculture Journal · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsLotus corniculatusTrefoilForageCichoriumAgronomyNeutral Detergent FiberBiologyFodderDry matterLotusLegumeMedicago sativaBotany

Abstract

fetched live from OpenAlex

As part of a study to evaluate alternative forages for farmed deer, we compared forage yields and quality of birdsfoot trefoil ( Lotus corniculatus ) and forage chicory ( Cichorium intybus ) with that of alfalfa ( Medicago sativa ) in north central Alberta, Canada. Despite similar plant densities among the three species, the foliar cover of chicory averaged 76%, 20% greater than alfalfa and 50% greater than trefoil. Alfalfa had higher plant height, dry matter yields, and crude protein concentrations compared to chicory and trefoil, leading to crude protein yields nearly double that of the other forages. Alfalfa also had superior over-winter persistence. Birdsfoot trefoil stands exhibited poor competitiveness in the year of establishment, as demonstrated by high weed and volunteer clover biomass. Chicory had lower neutral detergent fiber concentrations compared to the other forages, leading to a favorable neutral detergent soluble value of 590 g kg -1 DM, 6% greater than that of trefoil. In contrast, tannin concentrations were greatest in trefoil (nearly 60 g kg -1 DM), well above those in the other forages (<20 g kg -1 DM). These results highlight the potential of chicory for forage production, as well as the importance of mixing alfalfa with alternative forages to optimize forage yield and quality.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.617

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 studies0.0010.000
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 head0.050
GPT teacher head0.258
Teacher spread0.208 · 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 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

Citations13
Published2008
Admission routes3
Has abstractyes

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