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Record W2078574126 · doi:10.4141/p01-163

Banded herbicides and cultivation for weed control in potatoes (<i>Solanum tuberosum</i> L.)

2002· article· en· W2078574126 on OpenAlexvenueno aff
J. A. Ivany

Bibliographic record

VenueCanadian Journal of Plant Science · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsWeed controlAgronomyWeedHectareSowingBiologyCropYield (engineering)Agriculture

Abstract

fetched live from OpenAlex

Increasing costs of production have resulted in intensified efforts to reduce the amount of herbicides applied in potato production. This research evaluated the potential of applying herbicides in 30-cm-wide bands over the potato row in combination with cultivation between the potato rows to achieve weed control. At a moderate to high infestation of quackgrass and annual broadleaf weeds, control of quackgrass, corn spurry and wild radish was as effective with the banded herbicide + cultivation treatments as with the broadcast herbicide treatment. Potato marketable yields from the banded herbicide + cultivation treatments were comparable to the broadcast herbicide application treatment. A single cultivation at 21 d after planting (DAP) (at ground crack), 28 DAP (potatoes 5 to 10 cm tall) or 35 DAP (potatoes 10 to 15 cm tall) did not give adequate weed control, and potato yields were reduced by 30% or more at all times of cultivation compared to herbicide treatments. This study shows that acceptable weed control without effects on marketable yield is possible by using a 30-cm-wide herbicide band over the row followed by cultivation to remove weeds between the row. The amount of herbicide used per hectare with this technique is reduced by 66% compared to a broadcast herbicide application. Key words: Herbicide, potato, quackgrass, Elytrigia repens L. (Nevski), corn spurry (Spergula arvensis L.), wild radish (Raphanis raphanistrum L.), cultivation

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 score0.965
Threshold uncertainty score0.995

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 head0.016
GPT teacher head0.192
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

Citations15
Published2002
Admission routes1
Has abstractyes

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