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Record W6959443397 · doi:10.7939/r3-8ngg-s490

Herbicide Strategies for Control of Glyphosate-Resistant and -Susceptible Kochia (Bassia scoparia) in Chemical Fallow and Spring Wheat

2022· dissertation· en· W6959443397 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2022
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsGlyphosateDicambaBromoxynilWeed controlBiomass (ecology)WeedScopariaSummer fallow

Abstract

fetched live from OpenAlex

Kochia [Bassia scoparia (L.) A.J. Scott], the first known glyphosate-resistant weed in western Canada, is an abundant and troublesome summer annual tumbleweed. Yet, knowledge gaps exist in kochia management, specifically as to what herbicide and herbicide mixes are effective to control glyphosate-resistant (GR) and glyphosate-susceptible (GS) kochia in chemical fallow and spring wheat (Triticum aestivum L.). Kochia’s tolerance to saline soils, drought, and heat, as well as its ability to emerge early with multiple flushes, rapid growth, and late maturation, all contribute to its reproductive success and geographical expansion. This thesis research consisted of two field studies conducted in Alberta, Canada, from 2013 to 2015 and aimed to discover effective herbicidal control for GR and GS kochia in chemical fallow and spring wheat in western Canada. The most consistent control in chemical fallow (≥80% visual control in all environments with ≥80% biomass reduction in 2014) was observed with glyphosate + dicamba, glyphosate + dicamba/diflufenzopyr, glyphosate + saflufenacil, and glyphosate + carfentrazone + sulfentrazone. Reduced efficacy was observed for several herbicide mixtures when they were applied to GR compared with GS kochia accessions. Effective modes of action mixed with glyphosate include synthetic auxins (group 4), a combination of a synthetic auxin and an auxin transport inhibitor (group 19), or protoporphyrinogen oxidase inhibitors (group 14). The most effective and consistent treatments for kochia management in spring wheat included sulfentrazone applied pre-emergence and fluroxypyr/bromoxynil/2,4-D or pyrasulfotole/bromoxynil applied post-emergence. All of these treatments resulted in ≥90% visible control in all environments and ≥90% kochia biomass reduction compared with the untreated control in Lethbridge 2014 and 2015. MCPA/dichlorprop-p/mecoprop-p, dicamba/2,4-D/mecoprop-p, and dicamba/fluroxypyr resulted in acceptable control among environments (≥80% visible control in all environments and ≥80% kochia biomass reduction in Lethbridge 2014 and 2015); however, the latter two options caused unacceptable (>10%) wheat visible injury in Coalhurst 2014. Confirmations of auxinic herbicide-resistant kochia in western Canada, partly due to their increase of use on GR kochia in spring grains, will limit these herbicide options. If designed appropriately, an integrated herbicide program for kochia including mixing, rotating, and layering alternative herbicide modes of action could help mitigate further selection for herbicide resistance.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
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 head0.024
GPT teacher head0.274
Teacher spread0.251 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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