Feasibility of a wider row spacing and recommended nitrogen in no‐till wheat
Bibliographic record
Abstract
Abstract The optimal row spacing aims to maximize profitability by balancing the reduction in production costs from a wider row spacing against a potential decline in yield and increased weed pressure. A wider row spacing should increase area seeded per day, improve residue flow around seeder openers and the success of seeding between stubble rows. This study investigated the feasibility of a wider row spacing by studying the effects of row spacing (25, 30, 35, 40 cm) and N fertilizer rates (20, 40, 80, 120, 160 kg N ha−1) on development, yield, and quality of spring wheat (Triticum aestivum L.). The study was a two factorial in randomized complete block design. The experiment was conducted at Indian Head, SK, from 2013 to 2016. Row spacing affected plant, head, and seed density; however, the effects were generally inconsistent. One exception was biomass, which decreased as the row spacing increased in 3 of the 4 yr. Grain yield declined in 1 of the 4 yr, with the largest portion of the decrease occurring as the row spacing increased from 30 to 40 cm. As expected, increasing N rates produced greater grain yield, biomass, and grain protein. In conclusion, this study found that in most years the row spacing can be widened past 30 cm without a negative impact on grain yield in a no‐till cropping system. However, to determine the probability of a grain yield decrease as row spacing is increased, a larger study over a wider geographic region is needed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".