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Record W2025004692 · doi:10.4141/p05-228

Scheduling drip irrigation for ginseng (<i>Panax quinquefolius</i> L.) grown under straw and bark mulch

2007· article· en· W2025004692 on OpenAlexvenueno aff
R. C. Roy, B. R. Ball Coelho, A. J. Bruin, R. D. Reeleder, B. Capell

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsnot available
Fundersnot available
KeywordsMulchIrrigationStrawDrip irrigationAgronomyEnvironmental scienceBark (sound)Water contentGrowing seasonIrrigation schedulingSoil waterPlastic mulchMathematicsHorticultureBiologyForestryGeographySoil scienceEngineering

Abstract

fetched live from OpenAlex

Responses of ginseng to drip irrigation regimes and organic mulches were determined in two experiments beginning in 1998 and 1999. Treatments were four soil water content thresholds for irrigation: 0, 0.08, 0.12 and 0.16 m 3 m -3 (θ 0 , θ 8 , θ 12 , and θ 16 , respectively); combined with either straw or bark mulch. In its first growing season, ginseng did not require irrigation in 1999 or 2000. In subsequent years, irrigation was generally more frequent when applied at higher moisture threshold levels, but precipitation affected irrigation frequency under all treatments. Water use increased with crop age to 3yr, to about 65% of the requirement of unshaded horticultural crops. Seed yield from 3-yr-old plants under straw mulch in 2001 was greater in response to θ 12 and θ 16 than to θ 8 or θ 0 . Root yield response to irrigation threshold was linear for 2- and 3-yr- old plants under straw mulch in 2001, and quadratic for 3-yr-old plants under bark mulch in 2002. The optimal threshold to initiate drip irrigation was approximately 65% of field capacity. Key words: Root, soil water, water use

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)0.002
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 score0.753
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.233
Teacher spread0.207 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Plant ScienceSame topicIrrigation Practices and Water ManagementFrench-language works237,207