Effects of nitrogen fertilizer application on seed yield, N uptake, N use efficiency, and seed quality of Brassica carinata
Bibliographic record
Abstract
Johnson, E. N., Malhi, S. S., Hall, L. M. and Phelps, S. 2013. Effects of nitrogen fertilizer application on seed yield, N uptake, N use efficiency, and seed quality ofBrassica carinata. Can. J. Plant Sci. 93: 1073-1081. Ethiopian mustard (Brassica carinata A. Braun) is a relatively new crop in western Canada and research information on its response to N fertilizer is lacking. Two field experiments (exp. 1 at 3 site-years and exp. 2 at 4 site-years) were conducted from 2008 to 2010 in Saskatchewan and Alberta, Canada, to determine effect of N fertilizer application on Brassica carinata plant density, seed and straw yield, N uptake in seed and straw, N use efficiency (NUE), N fertilizer use efficiency (NFUE) and seed quality. N rates applied were 0 to 160 kg N ha-1 and 0 to 200 kg N ha-1 in exps. 1 and 2, respectively. Plant density was not affected by increasing N rate at 5 site-years but declined with high rates of N application at 2 site-years. Seed yield responded to applied N in 6 of 7 site-years, with the non-responsive site having a high total N uptake at the 0 kg N ha-1 rate (high Nt value). There were no sites where seed yields were maximized with the N rates applied. Response trends of straw yield and N uptake were similar to that of seed yield at the corresponding site-years. NUE and NFUE generally declined as N rate increased. Protein concentration in seed generally increased and oil concentration in seed decreased with increasing N rates. In conclusion, the responses of seed yield, total N uptake, NUE, and NFUE to applied N was similar to those reported in other Brassica species with the exception that a rate was not identified in which Brassica carinata yields were maximized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".