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, 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.
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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".