Optimizing weed and sucker control in hazelnut orchards with tiafenacil
Bibliographic record
Abstract
Hazelnut (Corylus avellana L.) plays a crucial role in the agricultural landscape of Oregon's Willamette Valley, and contributes to over 99% of total USA hazelnut production. Management of weeds and hazelnut suckers is a labor-intensive and time-consuming endeavor. Unfortunately, current strategies have significant limitations, including impractical and environmentally unfriendly options. Field studies were conducted in commercial hazelnut orchards across the Willamette Valley to address these challenges. The objectives were to evaluate the efficacy of low-toxicity herbicide tiafenacil as a weed and hazelnut sucker control and further to document hazelnut tolerance to repeated basal-directed applications of the herbicide. The results demonstrated that tiafenacil is safe to hazelnuts, causing no harm to the tree trunk or canopy. Moreover, there were no adverse effects on growth parameters or hazelnut yield. Tiafenacil at 50 g ai ha-1 outperformed carfentrazone, an existing benchmark, in managing Polygonum aviculare L., Daucus carota L., and Cirsium arvense L. Scop. However, tiafenacil at 12.5, 25, and 50 g ai ha-1 did not surpass the current and most effective benchmark, glufosinate, for weed control. Tiafenacil at 50 g ai ha-1 effectively managed suckers similar to manual removal and with superior efficacy to carfentrazone. Combining tiafenacil at 50 g ai ha-1 with glufosinate or 2,4-D improved sucker and weed control, suggesting that its efficacy might be enhanced in mixtures with other herbicides rather than as a standalone option. Importantly, tiafenacil exhibited excellent compatibility with 2,4-D and glufosinate, indicating its suitability for integration with various herbicides to improve weed and sucker control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".