Cultivar and fertilizer S rate interaction effects on canola yield, seed quality and S uptake
Bibliographic record
Abstract
Since canola has a high requirement for S, and canola cultivars have differences in growth rate and yield potential, a differential response of canola cultivars to soil S deficiency and S fertilization can be expected. A 3 site-year field study was conducted on S-deficient soils in northeastern Saskatchewan to determine response of four canola cultivars to S deficiency and S fertilization in terms of yield (seed and straw), seed quality (oil, protein and S concentration), and S uptake (seed and straw). Two Brassica napus L. cultivars (Quantum and AC Excel) and two B. rapa L. cultivars (Maverick and AC Parkland) were compared at 0, 5, 10, and 15 kg S ha-1 rates. Both the actual values of seed and straw yield and seed S uptake and the responses to S fertilization were greater for the B. napus than B. rapa cultivars. Optimal yield response for all the four cultivars occurred at the 10 kg S ha-1 rate, though seed quality and S uptake responded up to the 15 kg S ha-1 rate. Sulphur fertilization response was quadratic for seed and straw yield, seed oil and protein concentration, and S uptake in seed, while the response for seed S concentration and straw S uptake was inconsistent. In summary, in spite of differences in magnitude of the response of tested cultivars to S fertilization, the similar nature of the response and optimal yield at the same S rate indicated th at specific S fertilization recommendations for individual canola cultivars are unnecessary. Key words: Canola cultivars, oil concentration, seed quality, S rates, S uptake, yield
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".