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Record W2525681369 · doi:10.21273/hortsci.39.5.991

Reduced Tillage Alternatives for Machine-harvested Cucumbers

2004· article· en· W2525681369 on OpenAlexaff
S. Lonsbary, John O’Sullivan, Clarence J. Swanton

Bibliographic record

VenueHortScience · 2004
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTillageCucumisAgronomyConventional tillageMinimum tillageEnvironmental scienceCultural practiceNo-till farmingMathematicsBiologyHorticultureSoil waterSoil fertilityPoaceaeSoil science

Abstract

fetched live from OpenAlex

Cucumber ( Cucumis sativus L.) is grown using intensive tillage practices, which increase the cost of production and may lead to an increase in soil and water erosion. Research on alternative tillage practices for cucumber production has been limited primarily to exploring the benefits of no tillage. Alternative tillage practices, such as disking (one pass with a tandem disk) and zone tillage (one pass with a Trans-till) have not been investigated. Thus, the objective of this study was to compare the influence of reduced tillage practices on the growth, development, and yield of cucumbers. Seedling emergence varied between years, but was unaffected by a reduction in tillage, while cucumber leaf number, leaf area index, and vine growth were reduced by no tillage ( P ≤ 0.05). Total dry matter accumulation and days to 50% open flower varied with tillage. No-tillage plots produced an average of 34 g·m -2 of dry matter compared to 47 g·m -2 for conventional tillage plots and took 1 day longer to reach 50% flower. Although growth differences were observed under all reduced tillage treatments, no reduction in total yield was observed when compared with conventional tillage yields. Alternative reduced tillage practices, such as disking or zone tillage, were found to be viable options for successful cucumber production. These alternative practices will reduce the cost of production, provide growers with greater time flexibility and ease of land preparation, and reduce the potential for water and wind erosion.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.386

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 head0.236
Teacher spread0.225 · 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 designSimulation or modeling
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

Citations10
Published2004
Admission routes1
Has abstractyes

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