Yield, Seed Quality, and Sulfur Uptake of <i>Brassica</i> Oilseed Crops in Response to Sulfur Fertilization
Bibliographic record
Abstract
Field experiments were conducted in 2003, 2004, and 2005 on a S‐deficient Gray Luvisol (Boralf) soil near Star City, in northeastern Saskatchewan, to determine yield, seed quality and S uptake response of different Brassica (B.) oilseed species/cultivars to S deficiency and S fertilization. A total of 20 treatments were tested in a factorial combination of four oilseed crops (B. juncea canola cv. Arid, B. juncea canola cv. Amulet, B. juncea mustard cv. Cutlass, and B. napus cv. InVigor 2663 hybrid canola) and five rates of potassium sulfate fertilizer (0, 10, 20, 30, and 40 kg S ha−1). All B. species/cultivars responded positively for seed yield and most other parameters to S fertilizer in all 3 yr, but the magnitude of response varied with species/cultivar and year. Seed yield was highest with Cutlass juncea mustard in a dry year (2003), but was highest with InVigor 2663 hybrid canola in years with above‐average precipitation (2004 and 2005). Seed yield was usually maximized at the rate of 30 kg S ha−1 for all B. species/cultivars. Oil concentration in seed increased with S fertilization for all B. species/cultivars. There was a significant (albeit small) increase of protein concentration in seed due to S fertilization. Cutlass juncea mustard accumulated considerably high concentrations of glucosinolates in seed, but glucosinolate concentrations were low in other B. species/cultivars. Sulfur uptake in seed was highest with Cutlass juncea mustard in all years. The effects of S deficiency and applied S were more pronounced on seed than straw. In conclusion, S fertilizer requirements for optimum seed yield were similar for all the B. species/cultivars used in this study on S‐deficient soil, but higher yielding types of B. would produce greater seed yield by using S more efficiently.
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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 | 0.000 |
| 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) | 0.001 | 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".