Chemical Management Strategies for Halo Blight of Hop and In Vitro Sensitivity of <i>Diaporthe humulicola</i> Populations to Various Fungicide Classes
Bibliographic record
Abstract
Halo blight of hop, caused by Diaporthe humulicola, has increased in eastern North America since 2018. When left untreated, the disease can cause yield loss ranging from 17 to 56%. Currently, there are no fungicides registered for use on halo blight of hop. From 2020 to 2022, field trials were conducted using 10 fungicides registered for use on powdery and downy mildew of hop to determine their efficacy against halo blight. To validate field results, the effective concentration of fungicide required for 50% growth inhibition (EC50) value was determined for each active ingredient including flutriafol, tebuconazole + fluopyram, cyflufenamid, and trifloxystrobin + salicylhydroxamic acid (SHAM). Each fungicide tested had an EC50 value less than 50 ppm. A discriminatory dose was used to test the sensitivity of 206 D. humulicola isolates collected from the eastern United States and Canada in a poison agar assay. Results showed that tebuconazole + fluopyram decreased the incidence and severity of halo blight in the field. Also, this fungicide combination had EC50 values of 2.26 × 10−1 ppm and significantly reduced the growth of most of the isolates tested. Trifloxystrobin + SHAM decreased the presence of halo blight in the field trial, but some isolates were less sensitive in discriminatory dose testing. Our results show that fungicides in FRAC groups 3, 7, and 11 were the most effective to control halo blight. Analyses of field trials showed a positive correlation between the severity of early-season downy mildew infections and late-season halo blight infections.
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 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) | 0.001 | 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".