Barley yield and malt characteristics as affected by nitrogen and final irrigation timing
Bibliographic record
Abstract
Abstract Idaho is a major malt barley (Hordeum vulgare L.) producer in the United States. Production is concentrated in the semi‐arid Snake River Plain region of southern Idaho. Irrigation and fertilizer N applications are two of the most important managed factors. Research was conducted at the University of Idaho Kimberly Research & Extension Center near Kimberly, ID, to determine yield, grain quality, and malt characteristics as affected by N application rate (0, 56, 112, and 168 kg N ha−1) and final irrigation timing at Feekes 10.0 (boot; F10.0), Feekes 11.2 (soft dough; F11.2), and +7 d after Feekes 11.2 (+7F11.2). Irrigation termination at F10.0 resulted in decreased yields and unacceptable malt characteristics across N rates. Irrigation termination at F11.2 and +7F11.2 yielded 6,439 kg ha−1 at a fertilizer N application of 56 kg N ha−1, similar to higher N applications. Greater predicted yields up to 6,886 kg ha−1 were calculated by regression analysis with applications up to 147 kg N ha−1. Grain yield, protein, plumps, and test weights did not differ at any N rate for F11.2 or +7F11.2. Malt extract, free amino N, and diastatic power were similar for the F11.2 and +7F11.2 irrigations. Malt β‐glucan content did not differ up to 56 kg N ha−1 for any treatment, but reductions of up to 30 mg kg−1 were measured at higher N rates for the +7F11.2 irrigation. Results warrant further investigations into increased N applications and provide evidence of the effects of irrigation cutoff timing and N for malt barley.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".