Advanced agronomic practices to maximize feed barley (Hordeum vulgare L.) yield, quality, and standability in Alberta environments
Bibliographic record
Abstract
The grain yields of feed barley (Hordeum vulgare L.) have increased at a slower rate than the yields of other major crops in Alberta, and seeded barley acres have declined over the past 20 years. Agronomic management and cultivar specific responses to management may provide solutions to increase grain yields and address production constraints such as lodging and quality limitations. Field experiments were conducted in 2014, 2015, and 2016 at four rainfed and one irrigated site in Alberta to evaluate the effects of seeding rate, post-emergence N, the plant growth regulator chlormequat chloride (CCC), and foliar fungicides on feed barley production. A separate field experiment was conducted to evaluate the effect of an advanced agronomic management package comprised of post-emergence N, CCC, and dual foliar fungicide on 10 feed barley cultivars. The largest yield increases (up to 19%) occurred when post-emergence N was applied in irrigated or high precipitation conditions and when levels of N applied at seeding were relatively low. Foliar fungicides resulted in small (3%) yield increases in the low disease pressures encountered in the study. Some agronomic and yield responses to dual fungicide and CCC depended on seeding rate. Chlormequat chloride did not markedly reduce height and lodging. Genetic lodging resistance was the best tool for lodging reduction in the study. Advanced agronomic management increased grain yield by 9.3% across all cultivars that all responded similarly. The highest yielding and quality cultivars were two-row. Of concern, recently registered cultivars (2008-2013) demonstrated static or negative yield gains compared with cultivars registered up to 13 years prior (2000). The 9.3% yield increase from advanced management was three times larger than the genetic yield gains observed across 10 cultivars registered between 2000 and 2013.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 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 teacher head, 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".