Effects of genotype, weather, FHB fungicide, and pre-harvest glyphosate on grain quality of hard red spring wheat in western Canada
Bibliographic record
Abstract
Diverse growing season weather in western Canada has large effects on wheat quality. Management practices, such as pesticide application, may also affect wheat quality, but are largely unknown. This study measured pesticide application effects on grain quality for six hard red spring wheat cultivars over three growing seasons at four prairie locations. Each siteyear included (i) an untreated control, (ii) fungicide applied at anthesis for Fusarium head blight (FHB) control, (iii) pre-harvest glyphosate applied at physiological maturity, and (iv) a combination of both pesticides. Generally warmer and drier conditions in 2015 and 2017 compared to 2016 resulted in wheat with higher grades, test weight, thousand-kernel weight, and grain protein content but lower Fusarium damaged kernel (FDK) content. Siteyear, reflecting weather variation by location, was the major factor affecting grain quality, contributing from 39% to 77% of total variance. Rainfall variation was greater than that for air temperature and appeared to be the main weather factor affecting quality. Genotype had a significant impact on grain quality but contributed 1%–20% of total variance. The pesticide treatments had a significant effect on several quality parameters, but they contributed only 0.2%–2% to total variance, implying that they have no detrimental effect on wheat grain quality when applied as recommended. Fungicide significantly reduced FDK level in four of ten siteyears, all with high FDK levels, but not when applied at the low FDK siteyears. Fungicide for FHB control should be used only when weather is conducive to high FHB disease pressure.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".