Seeding rate effect on winter malting barley yield and quality
Bibliographic record
Abstract
Abstract Growth in the craft brewing industry has increased the demand for locally sourced malting barley (Hordeum vulgare L.) grain in the Eastern United States. However, most malting barley seeding rate recommendations are from the Northern Great Plains, the Pacific Northwest, and Western Canada. Therefore, seeding rate research was needed in the humid growing environment of the Eastern United States. The objective of this research was to identify the agronomic optimum seeding rate (AOSR) where grain yield is maximized, and identify the seeding rate that met or exceeded grain quality parameters. An experiment with five seeding rate treatments ranging from 1.9 to 6.2 million seeds ha–1 was established at eight site‐years in Ohio. Under normal growing conditions, the AOSR was 3.8–6.2 million seeds ha–1 (average 4.5 million seeds ha–1). When plants experienced winter injury, the AOSR was greater at 5.3–5.4 million seeds ha–1. Grain quality parameters of protein, germination, and deoxynivalenol all tended to improve with increasing seeding rate. Seeding rates of 4.5–4.7 million seeds ha–1 should maximize yield most years while meeting grain quality parameters. However, regions that experience winter temperatures <15°C without snow coverage should increase seeding rates due to increased chance of winter injury reducing plant stand.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".