Cultural practices to improve malt barley quality in the northeast with focus on the craft sector
Bibliographic record
Abstract
Demand is increasing for locally grown malt barley (Hordeum vulgare L.) in northeastern North America, driven primarily by growth in the craft beer sector. A multi-site experiment was conducted to evaluate how variety (V), seeding rate (S), and nitrogen (N) fertilizer affect malt quality in the northeast. Two barley varieties (Cerveza and Newdale), two seeding rates (200 and 400 seeds m−2), and five rates of actual applied N fertility (0, 30, 60, 90, and 120 kg ha−1) were tested at Charlottetown, PE, Canada, Ithaca, NY, US, Princeville, QC, Canada, and New Liskeard and Ottawa, ON, Canada. Basic agronomic data were collected from all environments including yield, thousand kernel weight, and hectoliter weight. Barley of suitable quality was micromalted and subjected to malt quality analysis. Both V and S resulted in small effects on malt quality, however, N had the greatest effect on most measured variables. Increased rates of N application resulted in increased yield, hectoliter weight, and thousand kernel weight but had a negative effect on most quality traits, especially with increased protein content, reduced fine extract, Kolbach index, and friability, though it increased wort β-glucans. This study shows that for most years at most sites, it is possible to achieve malt quality in the northeast; however, excessive protein and the prevalence of preharvest sprout damage are the main barriers. The results of this study have implications for increased malt barley production for the craft sector as well as potential access to commodity markets for northeastern producers.
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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 | 0.000 | 0.001 |
| 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".