Application of widely used fungicides does not necessarily affect grain yield, and incidence of Fusarium spp. and mycotoxins DON, HT-2 and T-2 in spring barley in northern climates
Bibliographic record
Abstract
Fungicides are widely used to reduce Fusarium infections and grain contamination by mycotoxins and increase the yield in cereals, but the efficacy of fungicide treatments in varying climates has not been systematically explored. Field experiments with Estonian spring barley (Hordeum vulgare L.) cv. ‘Maali’ were carried out in three successive years 2012–2014 with strongly varying weather conditions to study the effects of three fungicides, Folicur (active ingredient tebuconazole), Falcon Forte (prothioconazole, tebuconazole, spiroxamine) and Archer Top (fenpropidin, propiconazole), on the yield, incidence of Fusarium spp. and on the contamination of grain with mycotoxins DON, HT-2 and T-2. The fungicides were sprayed once a year at spring barley flowering time. The weather conditions during the three years of study were extremely different. The content of cycotoxin DON, HT2 and T2 was low. The spraying with fungicides had not a clear effect on the barley yield and 1 000 kernel weight, and the study year was primarily the main factor that affected barley yield (p<0.05) and 1 000 kernel weight (p<0.05). The impact of year together with fungicide treatment had a significant effect on the incidence of Fusarium spp. (p<0.05) and on the incidence of mycotoxin DON in barley kernels (p<0.001), but did not have a clear effect on the incidence of mycotoxins HT2 and T2.
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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) | 0.001 | 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".