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Record W3020634638 · doi:10.1007/s11104-020-04532-1

Root growth and turnover in perennial forages as affected by management systems and soil depth

2020· article· en· W3020634638 on OpenAlexafffund
Stéphanie Houde, Marie‐Noëlle Thivierge, Florian Fort, Gilles Bélanger, Martin H. Chantigny, Denis A. Angers, Anne Vanasse

Bibliographic record

VenuePlant and Soil · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversité LavalAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaMitacs
KeywordsAgronomyPerennial plantTopsoilRoot systemForageBiologyBiomass (ecology)Festuca arundinaceaPhalaris arundinaceaLolium perenneEnvironmental scienceSoil waterPoaceaeWetlandEcology

Abstract

fetched live from OpenAlex

Abstract Aims Extensive knowledge of perennial forage root systems is essential, given their critical role in below-ground C input. Methods Root length and diameter were quantified periodically from 2016 to 2018 with minirhizotrons in a field experiment with three forage management systems: mixture of timothy ( Phleum pratense L.) and tall fescue ( Festuca arundinacea Schreb.) fertilized with (i) dairy cattle slurry or (ii) calcic ammonium nitrate, and (iii) mixture of timothy, tall fescue, and alfalfa ( Medicago sativa L.) without N fertilization. Root biomass was measured yearly by coring. Results Management systems with the two fertilization sources did not differ in root elongation, but the management system with alfalfa resulted in a slower root elongation after the first defoliation and a lower root mortality in the fall. Root length turnover was greater in the topsoil with dairy cattle slurry than with calcic ammonium nitrate. Fine roots dominated the surface soil and coarse roots the deeper soil layers. Conclusions Root growth and mortality were more contrasted between systems that differed by the presence of alfalfa than by fertilizer source. As many root characteristics are drivers of soil C storage, the choice of perennial species in mixtures appears as a key management factor for sustainable farming systems.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.232

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 head0.011
GPT teacher head0.188
Teacher spread0.176 · 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

Citations37
Published2020
Admission routes2
Has abstractyes

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