Advanced Agronomic Practices to Maximize Feed Barley Yield, Quality, and Standability in Alberta, Canada. I. Responses to Plant Density, a Plant Growth Regulator, and Foliar Fungicides
Bibliographic record
Abstract
Core Ideas Chlormequat chloride did not markedly reduce height or lodging, but yield increased by 2% across environments. Chlormequat chloride improved barley quality through increased test weight in moderate stands (240 plants m −2 ). Foliar fungicides increased grain yield by 3% across environments, which overall had low disease pressure. Dual fungicide applications increased yield more than single applications only at the higher density (355 plants m −2 ). Greatest grain yield was achieved by 355 plants m −2 , a growth regulator, and dual fungicide applications. Barley ( Hordeum vulgare L.) yield has increased at a slower rate than other major crops in Alberta, and barley area has declined over the last two decades. Improved agronomic management may increase grain yield and address other production constraints such as lodging, disease, and quality limitations. Field experiments were conducted in 2014, 2015, and 2016 at four rainfed sites and one irrigated site in Alberta, Canada, to evaluate the effects of plant density (targeting 240 or 355 plants m −2 ), the plant growth regulator chlormequat chloride (CCC; 2‐chloroethyl‐trimethyl‐ammonium chloride), foliar fungicide application (at flag leaf, 2 wk later, or at both application [dual] timings), four rates of post‐emergence N, and the interaction of these factors on ‘Amisk’ feed barley. There was no post‐emergence N interaction with the other agronomic practices tested in this study. The CCC did not markedly reduce height or lodging, but increased grain yield by 2%. Test weight increased by 0.5 kg hL −1 with CCC at 240 plants m −2 . Foliar fungicides resulted in an average yield increase of 3% in the low disease conditions encountered in the study. Dual fungicide applications increased yield over single applications at the 355 plants m −2 density only. Combined, the most intensive agronomic practices (355 plants m −2 , CCC, and dual fungicide applications) provided a 7% grain yield increase compared to the low intensity control.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".