Seeding rate and sulfur drive field pea yields in the Maritime region of Canada
Bibliographic record
Abstract
The inclusion of pulse crops in Canadian rotations has the potential to improve cropping system efficiencies, reduce the overall amount of applied nitrogen, provide economic opportunities for producers, and reduce the overall carbon footprint of the cropping system. Although primarily grown in western Canada, many pulse species—field pea in particular, are well suited to temperate growing conditions in the Maritime region of Canada. A study was conducted over 2 years at Harrington, Prince Edward Island, and consisted of four field pea varieties including two yellow varieties (AAC Lacombe and CDC Saffron) and two green varieties (CDC Limerick and CDC Raezer) planted at three plant population densities: 75, 100, and 125 plants m−2. The study also measured the effects of nitrogen fertilizer applied pre-plant (0 kg ha−1 vs. 15 kg ha−1) and applied plant available sulfur (0 kg ha−1 vs. 25 kg ha−1). Overall, yellow pea varieties were higher yielding than green pea varieties, and there was a linear increase in yield with increased seeding rate. There were no significant effects of pre-plant nitrogen fertilizer on yield, although it did slightly increase seed protein. Applied sulfur had a positive effect on yield and a slightly negative effect on thousand seed weight. This experiment provides a recommendation for the optimal seeding rate (100 plants m−2) and fertility recommendations to achieve profitable yields growing field pea in the Maritime region of Canada.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".