Optimizing Nitrogen Use for Irrigated Waxy Barley
Bibliographic record
Abstract
Waxy barley (Hordeum vulgare L.) with high amylopectin has value as a food crop, but information on optimal N fertilization of furrow‐irrigated waxy barley is limited. Furrow‐irrigated field studies were conducted at Parma, ID, and Ontario, OR, with ‘Merlin’ and ‘Salute’ spring genotypes planted fall or spring during the 2006, 2007, and 2008 seasons as the main plots and N treatments as subplots. “Early” N as dry urea was applied preplant or late winter (0, 60, 120, and 180 lb/acre) and “late” N was applied at heading (0 or 40 lb N per acre) to selected early N treatments. Late N was applied as top‐dressed dry urea (DU), foliar fluid urea (FU), foliar urea‐ammonium nitrate (FUAN, Parma only) or FU applied to ethephon‐treated barley (FUE, Ontario only). The optimal early N rate for Salute at Ontario was 0 with severe lodging or 60 lb N per acre, as compared to 60 or 120 lb N per acre for Merlin. At Parma, varieties did not differ in their yield response to N and optimal early N, ranged from 60 to 120 lb/acre. Yield increased as much as 1488 lb/acre with late N and was unaffected by late N source. Late N did not increase lodging or reduce apparent N recovery despite more total fertilizer N available. Ethephon controlled lodging sufficiently to improve yield 1439 lb/acre. Late N may allow producers to reduce the early N that contributes to lodging, and to both reduced yield and apparent N recovery.
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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 | 0.000 | 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".