Building on a foundation: advances in epidemiology, resistance breeding, and forecasting research for reducing the impact of fusarium head blight in wheat and barley
Bibliographic record
Abstract
Fusarium head blight (FHB) is a major fungal disease that contributes to severe economic losses for wheat and barley production in Canada and other parts of the world. Rapid developments in molecular biology over the past three decades have improved the ability to devise predictive management tools to combat the effects of the disease. Important aspects of Fusarium species in terms of the epidemiology associated with FHB in wheat and barley have been reported. The role of mycotoxin production in the epidemiology of the disease is beginning to receive much needed research attention. Evolutionary factors and the use of fungicides have resulted in more virulent forms of the FHB pathogens. Advances in next-generation sequencing technologies, including whole genome sequencing (WGS), genome-wide association studies (GWAS), genotyping by sequencing (GBS) and RNA sequencing (RNA-Seq) have facilitated the selection of resistant-breeding lines through marker-assisted selection. Many quantitative trait loci (QTL) associated with moderate disease resistance have been identified in wheat and barley. Changes in weather conditions play an important role in FHB epidemics and dissemination, thus a systematic and long-term research approach is needed to provide effective forecasting and risk assessment models. This review discusses the history and epidemiology of FHB pathogens in wheat and barley at the global level, as well as potential plant defence mechanisms, the recent progress made in resistance breeding, and modern tools utilized in disease prediction. It also provides future directions for improving the management of the disease with these two important cereals.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".