Impact of Fungicide Applications on Sweetpotato Roots and Slips for Managing Black Rot Disease ( <i>Ceratocystis fimbriata</i> ) and Their Effect on Pesticide Residue Levels
Bibliographic record
Abstract
, the causal agent of black rot, remains a major concern for sweetpotato producers and is commonly managed through the application of fungicides. As a result of the European Union's (EU) restricted residue tolerances for import products treated with pesticides, the use of fungicides for management of sweetpotato diseases is limited. Identifying fungicides and application practices that ensure disease-free sweetpotatoes while meeting export residue requirements is critical for effective disease management and export marketability. Field experiments were executed in 2022 and 2023 to quantify residue values of three active ingredients (thiabendazole, azoxystrobin, and difenoconazole) when applied at either bedding, transplant, or both bedding and transplant when managing sweetpotato black rot. Root and vine samples were collected at harvest to analyze the detectable residue levels of the applied active ingredients at different stages of sweetpotato production. High-performance liquid chromatography analyses revealed that the average detected residues for all the tested active ingredients and application timings fell under the maximum residue level thresholds for the United States, Canada, and the EU but not the United Kingdom. In the field experiments, fungicide treatments were not significantly different from nontreated plots for plot vigor, percentage disease incidence, or yield. Although the residues from the three tested products in this study were not a concern for U.S., EU, and Canadian markets when applied during sweetpotato field production, further research is needed to determine their potential as an effective management tool for sweetpotato black rot.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".