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Record W4392783776 · doi:10.5539/jas.v16n3p39

What Are the Most Efficacious Herbicides Applied Preplant for Control of Multiple-Herbicide-Resistant Canada Fleabane in Corn?

2024· article· en· W4392783776 on OpenAlexafffundvenueabout
Nader Soltani, Christian Willemse, Peter H. Sikkema

Bibliographic record

VenueJournal of Agricultural Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural AffairsMinistry of Agriculture, Food and Rural AffairsGrain Farmers of Ontario
KeywordsAgronomyWeed controlField cornBiologyZea mays

Abstract

fetched live from OpenAlex

New weed management strategies are needed to effectively control multiple-herbicide-resistant (MHR) Canada fleabane in corn. Five experiments were established in growers’ corn fields with confirmed MHR Canada fleabane to determine the efficacy of various herbicides applied preplant (PP). In 2021 environments, glyphosate + isoxaflutole + atrazine, glyphosate + isoxaflutole/diflufenican + atrazine, glyphosate + S-metolachlor/atrazine/mesotrione/bicyclopyrone, glyphosate + mesotrione + atrazine, glyphosate/dicamba + isoxaflutole/diflufenican, glyphosate/dicamba + isoxaflutole/diflufenican + atrazine, and glyphosate + saflufenacil/dimethenamid-p provided excellent control (90-100%) of MHR Canada fleabane but glyphosate + S-metolachlor/mesotrione/bicyclopyrone and glyphosate + S-metolachlor/atrazine/mesotrione controlled MHR Canada fleabane 77-84% and 87-96%, respectively at 4, 8, and 12 weeks after application (WAA). Herbicide tankmixes evaluated reduced MHR Canada fleabane density and biomass 91-100%. In 2022 environments, all glyphosate tankmixes evaluated provided 97-100% control, 99-100% density reduction, and 100% biomass reduction of MHR Canada fleabane in corn. In 2021 and 2022 environments MHR Canada fleabane interference reduced corn yield 41 and 32%, respectively; reduced MHR Canada fleabane interference with all herbicide treatments resulted in corn yield similar with the weed-free control. Results of this study indicate that glyphosate + isoxaflutole + atrazine, glyphosate + isoxaflutole/diflufenican + atrazine, glyphosate + S-metolachlor/atrazine/mesotrione/bicyclopyrone, glyphosate + mesotrione + atrazine, glyphosate/dicamba + isoxaflutole/diflufenican, glyphosate/dicamba + isoxaflutole/diflufenican + atrazine, and glyphosate + saflufenacil/dimethenamid-p provide excellent and consistent control of MHR Canada fleabane. However, glyphosate + S-metolachlor/atrazine/mesotrione and glyphosate + S-metolachlor/mesotrione/bicyclopyrone do not provide consistent control of MHR Canada fleabane in corn.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.212
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2024
Admission routes4
Has abstractyes

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