Effect of cultivar and agronomic management on feed barley production in Alberta environments
Bibliographic record
Abstract
The area sown to barley (Hordeum vulgare L.) in Alberta, Canada, and the rate of yield increase relative to other major crops have declined in recent decades. Advanced agronomic management of feed barley may increase the seeded area and differentially influence cultivar performance. Field experiments were conducted from 2014 to 2016 at 11 rainfed and three irrigated environments in Alberta to evaluate the performance of 10 feed barley cultivars under standard and advanced agronomic management. Advanced management included supplemental postemergence N, the plant growth regulator chlormequat chloride, and two foliar fungicide applications. Cultivars responded similarly to management in the low disease pressure environments encountered in the study. The two-row cultivars CDC Austenson, Xena, and CDC Coalition were the highest yielding overall, while Champion had the greatest yields in moisture-limited environments. The feed grain quality of two-row cultivars was superior to six-row cultivars. Recently released six-row cultivars were among the lowest yielding. Negative or static yield increases were observed for all newer cultivars (2006–2013 registrations) compared with the older cultivar, Xena (2000 registration). In comparison, the overall 9.3% yield increase from advanced management was notable. Advanced management yield increases were greater (8%–18%) in environments with 251–502 mm of precipitation and smaller (1%–3%) in moisture-limited environments. Management had negligible effects on lodging and grain quality. Optimal yield and quality were achieved with the two-row cultivars, CDC Austenson, Xena, or CDC Coalition, and advanced management in high precipitation environments. In environments with less precipitation, optimal yields were achieved with Champion and standard management.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".