The effects of pre-harvest glyphosate rate and timing on yield and pre-malt quality of malting barley
Bibliographic record
Abstract
The production of barley cultivars with malting and brewing quality characteristics is subject to strict grading and technical standards for the end-use market. Environmental and management factors can significantly alter grain quality, and the qualities required for malting. Crop and kernel uniformity are critical factors where variability can exceed the tolerance for meeting malt quality. A practice to address variations in crop maturity is the application of pre-harvest glyphosate. Pre-harvest glyphosate applications can, however, alter malting characteristics in barley, and, if mis-timed, can also reduce yield. A 4-year study at five locations in Alberta and Saskatchewan from 2013 to 2017 was conducted to determine the effects of pre-harvest glyphosate applications on malting barley characteristics. Glyphosate was applied at 900 and 1125 g ae ha −1 on malting barley cultivars ‘CDC Meredith’ and ‘AC Metcalfe’ malting barley at soft dough, hard dough, and physiological maturity growth stages. Yield reductions of up to 12% were observed from glyphosate applications at soft dough, and yield was maximized with applications at physiological maturity. Glyphosate application, at two rates, reduced percentage plump kernels, but did not affect kernel weight or protein concentration. The growth stage of barley plants did not provide an accurate indicator of seed moisture levels at the time of application, which motivated our conclusion that glyphosate applications can be mistimed by inaccurate indicators. The results motivate our questioning of the utility of pre-harvest glyphosate applications, given the adverse effects to barley yield and quality observed, even when applied according to the label instructions.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 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".