Weather‐based models for forecasting <scp>Fusarium head blight</scp> risks in wheat and barley: A review
Bibliographic record
Abstract
Abstract Fusarium head blight (FHB) is one of the most devastating crop diseases worldwide, significantly reducing the yield and quality of small‐cereal crops such as wheat and barley when favourable weather conditions exist during anthesis. Additionally, FHB‐associated mycotoxins significantly impact global food and feed safety. Controlling FHB with fungicides applied near anthesis reduces visual FHB symptoms and associated mycotoxin production, thereby lowering disease‐related costs. However, when weather conditions are unfavourable for FHB occurrence, fungicide application can be costly and environmentally undesirable. Thus, fungicides should be used sparingly only when the pathogen is present and weather conditions are favourable. Modelling of FHB risk using weather data has grown rapidly in recent decades and plays an essential role in integrated crop disease management. In this review, several weather‐based FHB models are selected and described in detail. The models were developed globally for assessing the real‐time risk of FHB epidemics in various regions/countries. Most of these models are site‐specific and predict FHB visual observations such as the incidence and severity of FHB, Fusarium‐damaged kernels (FDK), and also deoxynivalenol (DON) levels. The review also highlights the limitations of these existing models, including their narrow applicability, low accuracy for high‐risk contamination situations, and omissions of certain factors. Also discussed are potential avenues for improvement and enhanced predictive capabilities including consideration of additional disease risk factors as well as a broader range of varieties. These predictive models can assist producers, regulatory agencies, and industry to mitigate potential food and feed security and safety concerns.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".