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Record W4229452648 · doi:10.1017/wsc.2022.23

Interaction between 4-hydroxyphenylpyruvate dioxygenase–inhibiting and reactive oxygen species–generating herbicides for the control of annual weed species in corn

2022· article· en· W4229452648 on OpenAlexafffundabout
John C. Fluttert, Nader Soltani, Mariano Galla, David C. Hooker, Darren E. Robinson, Peter H. Sikkema

Bibliographic record

VenueWeed Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of Guelph
FundersGrain Farmers of OntarioOntario Agri-Food Innovation AllianceUniversity of Guelph
KeywordsGlufosinateMesotrioneBromoxynilBentazonLambsquartersProtoporphyrinogen oxidaseAtrazineTriclopyrDigitaria sanguinalisEchinochloa crus-galliWeedChenopodiumEchinochloaWeed controlBiologyAbutilonBotanyAgronomyGlyphosatePesticideBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Abstract The complementary modes of action of 4-hydroxyphenylpyruvate dioxygenase (HPPD) and photosystem II (PSII) inhibitors have been credited for the synergistic weed control improvement of several species. Recent research discovered that reactive oxygen species (ROS) generation and subsequent lipid peroxidation is the cause of cell death by the glutamine synthetase inhibitor glufosinate. Therefore, a basis for synergy exists between glufosinate and HPPD inhibitors, but the interaction has not been well reported. Four field experiments were conducted in Ontario, Canada, in 2020 and 2021 to determine the interaction between HPPD-inhibiting (mesotrione and tolpyralate) and ROS-generating (atrazine, bromoxynil, bentazon, and glufosinate) herbicides on control of annual weed species in corn (Zea mays L.). The ROS generators were synergistic with the HPPD inhibitors and provided ≥95% control of velvetleaf (Abutilon theophrasti Medik.), except for tolpyralate + glufosinate, which was additive at 8 wk after application (WAA) and provided 87% control. Tank mixes of HPPD inhibitors plus ROS generators were synergistic for the control of common ragweed (Ambrosia artemisiifolia L.), except for tolpyralate + glufosinate, which was antagonistic at 8 WAA. Tolpyralate + glufosinate was antagonistic for the control of barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.] and Setaria spp. at 8 WAA. Common lambsquarters (Chenopodium album L.) control at 8 WAA was synergistic and ≥95% with mesotrione plus atrazine, bromoxynil, or glufosinate and with tolpyralate plus bromoxynil or bentazon. Herbicide tank mixes were generally additive for the control of wild mustard (Sinapis arvensis L.) at 8 WAA, except for the synergistic tank mixes of tolpyralate plus atrazine or bromoxynil; however, each tank mix provided 97% to 100% control of S. arvensis. Results from this study demonstrate that co-application of ROS generators with mesotrione or tolpyralate controlled all broadleaf weed species >90% at 8 WAA, with the exceptions of A. artemisiifolia and C. album control with tolpyralate + glufosinate. Mesotrione plus PSII inhibitors controlled E. crus-galli and Setaria spp. 48 to 68 percentage points less than tolpyralate plus the respective PSII inhibitor at 8 WAA; however, mesotrione + glufosinate and tolpyralate + glufosinate controlled the grass weed species similarly.

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)0.000
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 score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.240
Teacher spread0.215 · 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

Citations4
Published2022
Admission routes3
Has abstractyes

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