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Record W2868119688 · doi:10.5539/jas.v10n8p1

Impact of Acetic Acid Concentration, Application Volume, and Adjuvants on Weed Control Efficacy

2018· article· en· W2868119688 on OpenAlexvenueno aff
Charles L. Webber, Paul M. White, James W. Shrefler, Douglas J. Spaunhorst

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAcetic acidWeedWeed controlDigitaria sanguinalisAgronomyAllelopathyBiologyHorticultureGerminationBiochemistry

Abstract

fetched live from OpenAlex

Acetic acid (CH3COOH) is produced naturally through anaerobic fermentation (vinegar) or synthesized through various industrial chemical methods. The primary components of vinegar are water and acetic acid. Acetic acid can destroy cell membranes, which then can result in plant tissue desiccation and plant death. Therefore, vinegar has the potential as a natural contact herbicide for the control of weeds in organically produced crops. Additional information is needed to determine the influence of acetic acid concentration, application volume, and adjuvants on weed control. Typically, household vinegar contains 5% acetic acid and greater acetic acid concentrations are available commercially. Field research was conducted in southeast Oklahoma (Lane, OK) to determine the effect of acetic acid concentrations, application volumes, and adjuvants on weed control efficacy. The factorial experimental design included three acetic acid concentrations (0, 5 and 20%), two sprayer application volumes (187 and 935 L/ha), three adjuvants (none, orange oil, and canola oil), and one weedy-check. The experiment was repeated twice. Visual weed cover and control ratings were collected 4 days after treatment. The experiment had very high weed densities with multiple grass and broadleaf weed species. The weedy check average weed cover percentages were 98% total weeds, 53% grass, 44% broadleaf weeds, 52% large crabgrass (Digitaria sanguinalis (L.), 25% carpetweed (Mollugo verticillata L.), and 14% cutleaf evening primrose (Oenothera laciniata Hill). Total weed control ranged from 0% control (no acetic acid) to 74% control (20% acetic acid, 935 L/ha, & canola oil). Acetic acid was more effective in controlling broadleaf weeds than in controlling grasses. Optimum total grass and crabgrass weed control occurred with 20% acetic acid applied at 935 L/ha, resulting in weed control that ranged from 44% to 63%. Broadleaf weed control was 84% or greater for plots receiving either 10% acetic acid applied at 935 L/ha or 20% acetic acid applied at 187 or 935 L/ha. In addition, 5% acetic acid applied at 187 L/ha provided good cutleaf evening primrose control (77% to 90%). When averaged across application volumes (187 and 935 L/ha) and adjuvants (none, orange oil, and canola oil), weed control increased for all species as acetic acid concentrations increased from 5% to 20%. When averaged across acetic acid concentrations and adjuvants, weed control increased as application volumes increased from 187 to 935 L/ha. Individual comparisons among adjuvants within acetic acid concentrations and application volumes showed little or no advantage to adding either orange oil or canola oil to vinegar spray solutions.

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 score0.982
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.249
Teacher spread0.240 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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