Effects of seeding rate, nitrogen rate and cultivar on barley malt quality
Bibliographic record
Abstract
BACKGROUND: Crop management tools have been shown to affect barley kernel size and grain protein content, but the direct effect on malt quality is not well understood. The present study investigated the effect of seeding rate, nitrogen fertilisation and cultivar on malt quality. RESULTS: Higher seeding rates produced barley with less grain protein and smaller, more uniformly sized kernels. The small, uniformly sized kernels modified more completely, leading to malt with higher extract and lower wort β-glucan than malt from low-seeding-rate barley. Increasing rates of nitrogen fertilisation caused grain protein levels to increase, which limited endosperm modification and reduced malt extract levels. AC Metcalfe showed better modification and higher malt extract than CDC Copeland, but CDC Copeland had better protein modification at higher fertilisation rates, which resulted in less reduction of malt extract as nitrogen rate increased. CONCLUSION: Higher seeding rates reduced kernel size and grain protein levels without compromising malt extract owing to better endosperm modification of the more uniformly sized kernels. Negative effects of higher nitrogen rates on malt quality can be reduced through development of cultivars with improved ability to modify protein during malting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".