The effect of cultivar, seeding rate and applied nitrogen on Brassica carinata seed yield and quality in contrasting environments
Bibliographic record
Abstract
Pan, X., Caldwell, C. D., Falk, K. C. and Lada, R. 2012. The effect of cultivar, seeding rate and applied nitrogen on Brassica carinata seed yield and quality in contrasting environments. Can. J. Plant Sci. 92: 961-971. The unremitting growth of oilseed demand makes it necessary to explore alternative oilseed crops to meet this requirement. This study evaluated the effects of genotype, seeding rate and nitrogen (N) supply on the seed yield and quality of oilseed Brassica carinata A. Braun in three contrasting environments (Truro, NS, Harrington, PE and Saskatoon, SK). Useful genetic variation in agronomic and seed quality characteristics was found among these 10 B. carinata genotypes and genotype selection requires location specific recommendation. Line 050488EM had consistently good yield in both Nova Scotia and Prince Edward Island, while line 070768EM displayed better yield stability across 2 yr in Saskatchewan. Because of the high degree of compensatory ability to low plant population, maximum seed yield of B. carinata can be achieved over the range from 34 to 114 plants m-2. No significant difference in oil and protein content of seed due to seeding rates was observed. The linear increase in seed and oil yield with increased N rate up to 150 kg ha-1 indicates that B. carinata is highly responsive to applied N. Increases in N supply resulted in a decrease in oil content and a corresponding increase in protein content in all experiments. In summary, the findings of this study provide convincing evidence of the agronomic adaptation of B. carinata to all three locations.
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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".