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Record W4408808574 · doi:10.3389/fagro.2025.1568894

Optimizing weed and sucker control in hazelnut orchards with tiafenacil

2025· article· en· W4408808574 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFrontiers in Agronomy · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSuckerWeed controlWeedBiologyAgronomyAgroforestryHorticultureZoology

Abstract

fetched live from OpenAlex

Hazelnut ( Corylus avellana L.) plays a crucial role in the agricultural landscape of Oregon's Willamette Valley, where weed and sucker management are labor-intensive and time-consuming endeavors. Current control strategies are either costly but effective, ineffective, or environmentally unfriendly. Tiafenacil, a relatively new herbicide, could be an effective solution. Field studies were conducted in commercial hazelnut orchards across the Willamette Valley to evaluate the efficacy of tiafenacil for weed and hazelnut sucker control. The results confirmed that tiafenacil applied three times per season up to 200 g ai ha -1 did not injure tree trunk or canopy and had no adverse effects on growth parameters, chlorophyll fluorescence, or yield. Tiafenacil at 50 g ai ha -1 outperformed carfentrazone 35 g ai ha -1 in controlling prostrate knotweed ( Polygonum aviculare L.), wild carrot ( Daucus carota L.), and Canada thistle ( Cirsium arvense L. Scop). However, tiafenacil up to 50 g ai ha -1 was less effective than glufosinate 1,050 g ai ha -1 for weed control. Tiafenacil at 50 g ai ha -1 effectively managed suckers comparable to manual removal and with superior efficacy to carfentrazone. Tiafenacil at 50 g ai ha -1 combined with glufosinate or 2,4-D 1,060 g ai ha -1 improved sucker and weed control compared with tiafenacil alone at the same rate, suggesting that its efficacy is enhanced in mixtures. Importantly, tiafenacil exhibited excellent compatibility with 2,4-D and glufosinate, making it a practical option for improving weed and sucker control strategies. For growers, incorporating tiafenacil into their management programs—either as a standalone treatment or in combination with glufosinate or 2,4-D—offers an effective alternative to manual sucker removal while maintaining strong weed control. These findings support tiafenacil as a valuable addition to hazelnut management programs, especially when used in combination with other herbicides for enhanced sucker and weed control without compromising tree health. While no antagonistic effects were observed when tiafenacil was mixed with glufosinate or 2,4-D, further research is necessary to explore potential interactions with other herbicides. Additionally, the economic viability of herbicide combinations should be evaluated before broad adoption.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.305

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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