Yield variability in Phalaris canariensis L. due to seeding date, seeding rate and nitrogen fertilizer
Bibliographic record
Abstract
May, W. E., Lafond, G. P., Gan, Y. T., Hucl, P., Holzapfel, C. B., Johnston, A. M. and Stevenson, C. 2012. Yield variability in Phalaris canariensis L. due to seeding date, seeding rate and nitrogen fertilizer. Can. J. Plant Sci. 92: 651-669. Concern over the year-to-year and field-to-field variability in grain yield has consistently been expressed by annual canarygrass growers in Saskatchewan. The objectives of these studies were to understand the effects of a delayed seeding date (0, 15, 30 and 45 d), seeding rate (15, 25, 35, 45, and 55 kg ha-1 of seed) and applied N fertilizer (20, 40, 60, 80, and 100 kg N ha-1) on the development and yield of annual canarygrass, to improve recommendations of best management practices in annual canarygrass and to determine the impact of these factors on yield variability in annual canarygrass. To address these objectives, three single factor field experiments were conducted, at a number of sites in Saskatchewan from 1998 to 2001. Seeding date had a large effect on grain yield. Grain yield decreased as seeding was delayed by 30 and 45 d from early May. Seeding rate had a small effect on grain yield. The response curve was very shallow peaking at approximately 1310 kg ha-1 at a seeding rate of 45 kg ha-1. Variation in grain yield tended to decrease as the seeding rate increased. There was a small increase in grain yield with the addition of nitrogen fertilizer. The response curve estimated a maximum yield of 1215 kg ha-1, which was obtained with a nitrogen rate of 78 kg ha-1. The majority of the increase was between 20 and 40 kg N ha-1, with a 2.3 kg ha-1 increase in grain yield for each kg of fertilizer N in that range of rates. There was a slight increase in grain yield as the nitrogen rate increased above 40 kg ha-1 but the variation in grain yield also increased reducing the incentive for growers to use N rates above 40 kg ha-1. Seeding date had a large effect on seed yield and could impact yield variability while seeding rate and nitrogen rate did not have a large effect on seed yield or yield variability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".