Effects of genotype, weather, and select pesticides on flour quality, gluten strength, and protein composition of hard red spring wheat in Western Canada
Bibliographic record
Abstract
The impact of crop management practices on wheat quality and gluten strength has received relatively little attention in the literature. The main objective of this study was to evaluate the effects of two widely used pesticides (fungicide and glyphosate) and their interactions with growing conditions and genotype on the leading commercial class of wheat in Canada. Six Canada Western Red Spring (CWRS) wheat varieties were grown in replicated field trials at four locations across the Canadian prairies over three growing seasons to capture a wide range of environmental conditions. Four pesticide treatments comprised a control (untreated), prothioconazole/tebuconazole fungicide applied at anthesis to mitigate effects of Fusarium head blight (FHB), glyphosate applied at physiological maturity, and application of both fungicide and pre-harvest glyphosate. Basic milling quality of samples was evaluated and gluten strength was assessed by dough mixing and gluten protein composition. Increasing level of precipitation was associated with reduced gluten strength based on dough properties and reduced content of high molecular weight polymeric glutenin and could be the effect of higher Fusarium damaged kernels (FDK) in wetter growing seasons. Siteyear and genotype were the most significant factors affecting all quality parameters. In contrast, pesticide treatments had minimal or no effects and were not statistically significant for any dough mixing or protein quality parameter. Thus, when used at recommended rates and timing, the pesticide treatments were not significant sources of variation of gluten strength for CWRS wheat. In contrast, growing season weather and wheat genotype were much more important factors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".