Agronomic Responses of <i>Brassica carinata</i> to Herbicide, Seeding Rate, and Nitrogen on the Northern Great Plains
Bibliographic record
Abstract
Brassica carinata A. Braun seeds contain high erucic and linolenic acids that are desirable for biofuel and biopolymer products. However, key agronomic practices to maximize carinata productivity on the Northern Great Plains are not well known. Here, we investigated the response of seed yield and quality to three key agronomic factors—herbicide, seeding rate, and N. The factorial experiment, conducted in nine environments (site‐years) on the Canadian prairies from 2014 to 2016, included two weed control options (weedy vs. weed‐controlled), three levels of N application (low = 32–97 kg N ha −1 , medium = 75–198 kg N ha −1 , and high = 117–297 kg N ha −1 ), and five seeding rates (50, 100, 150, 200, and 300 seeds m −2 ). On average, weed biomass with the herbicide‐applied treatment was 245 kg dry matter ha −1 , significantly lower than the 1338 kg dry matter ha −1 with the non‐herbicide treatment, leading to 37% greater seed yield. In four of the nine environments, medium and high N fertilizer rates increased seed yield by 29 and 45%, respectively, compared with the low‐N treatment, but N fertilizer did not affect yield in the remaining five environments. Increasing seeding rates decreased weed biomass and increased seed yield. Seed oil and protein contents ranged from 37.2 to 48.5% and 22.6 to 36.3%, respectively, and these two traits had an inverse linear relationship. These results demonstrate that carinata may be well adapted to the Northern Great Plains as an alternative oilseed crop, and that seed yield and quality responses to key agronomic factors can vary with environmental conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".