Niger Response to Nitrogen and Seeding Depth in the Northern Great Plains
Bibliographic record
Abstract
Core Ideas Determine the effect of nitrogen fertilizer and seeding depth on the development and yield of niger. There was a linear increase in grain yield, 76 kg ha –1 as the N rate increased from to 10 to 110 kg N ha –1 . Seeding depth impacted plant density and visible row, but the niger compensated and grain yield was not affected by seeding depth. A niger ( Guizotia abyssinica (L.f.) Cass.) cultivar has been selected to grow on the Northern Great Plains of North America; however, agronomic management practices in a no‐till cropping system regarding seeding depth and nitrogen (N) fertilization have yet to be developed for this region. Two experiments were conducted at Indian Head, SK, Canada from 2011 to 2017 to determine the response of niger to varying N fertilizer rates and seeding depths. Five rates of N (10, 35, 60, 85, and 110 kg N ha −1 ) and four seeding depths (surface, 0.6, 1.27, and 2.5 cm below soil surface) were applied in separate single factor experiments. Nitrogen fertilizer rate affected niger development. As N rate increased, there was a linear delay in the appearance of visible rows. Plant height and kernel weight were affected by N rate and year, grain yield was affected by year, and there was a linear increase of 18% in grain yield as the N rate increased from 10 to 110 kg N ha −1 . Niger plant density was affected by seeding depth, where plant density increased linearly with decreasing seeding depth in 2014, 2015, and 2017, while there was a negative quadratic relationship in 2016. This effect on plant density did not impact grain yield. In conclusion, niger grain yield benefitted from increased levels of N fertilizer and that growers can use a wide range of seeding depths to place niger seed in soil moist enough to support germination and emergence.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".