The role of genetics, growth habit, and cultural practices in the mitigation of Fusarium head blight
Bibliographic record
Abstract
Field trials were conducted under natural infection and artificial inoculation from 2012 to 2014 at seven sites across the Canadian prairies to determine genetic and management effects on Fusarium head blight (FHB) in wheat production systems. A system of management, which consisted of (1) a control of no fungicide was compared with (2) the seed treatment (ST) thiamethoxam + difenoconazole + metalaxyl-M + S-isomer, (3) an in-crop foliar fungicide (tebuconazole + prothioconazole), or (4) ST + foliar fungicide, was integrated with four wheat cultivars of contrasting growth habits and levels of FHB resistance. Results indicated the cultivars expressing improved FHB resistance, Carberry (spring wheat) and Emerson (winter wheat), were superior over susceptible cultivars, Harvest (spring wheat) and CDC Falcon (winter wheat), in reducing Fusarium-damaged kernel (FDK) and deoxynivalenol (DON) levels, and displayed higher yield under high Fusarium pressure. Winter wheat displayed higher overall yield, with Emerson producing the highest and most stable yields across environments. Application of foliar fungicide, with or without the ST, increased grain yield, seed mass, and test weight; and lowered FDK and DON. Seed treatment alone increased test weight, spring plant density of both winter wheat varieties, and kernel weight in Emerson. A management strategy of foliar fungicide and (or) ST + foliar fungicide generally produced higher yields with greater stability, particularly for susceptible cultivars in high FHB environments. The results of this study reinforce that integration of FHB-resistant cultivars with appropriate cultural practices is required to reduce the risk of FHB and optimize grain yield, and is further enhanced with a winter vs. spring growth habit.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".