The Effects of Chloride and Potassium Nutrition on Seed Yield of Annual Canarygrass
Bibliographic record
Abstract
The year‐to‐year variability of seed yield in annual canarygrass ( Phalaris canariensis L.) is a major concern among growers. A field experiment was conducted at 13 site‐years across Saskatchewan to determine the response of annual canarygrass seed yield to K and Cl, and to provide better recommendations to producers on the use of KCl fertilizer in annual canarygrass based on soil test results. Potassium did not affect the yield or development of annual canarygrass over a range of 155 to 717 kg K ha −1 in the top 15 cm of soil. Chloride had a large impact on annual canarygrass seed yield; seed yield increased by approximately 24% when Cl was added in the form of KCl or CaCl 2 when averaged across all sites. The seed yield increased because the application of Cl increased panicle size (seeds panicle −1 ). The magnitude of the response tended to increase as level of Cl in the soil decreased. Annual canarygrass growers need to measure Cl when using soil tests to determine fertilizer requirements. It is recommended that 9.1 kg Cl ha −1 in the form of 20 kg ha −1 of KCl be applied when the Cl level in the surface soil (0–15 cm) is below 70 kg Cl ha −1 . The findings encourage growers to conduct individual field test strips to determine the strength of the Cl response.
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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 | 0.000 | 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 | 0.000 | 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".