Row Spacing and Nitrogen Fertilizer Effect on No‐Till Oat Production
Bibliographic record
Abstract
A major challenge in agriculture is to enhance crop production in an environmentally sustainable fashion to meet the needs of a growing population given the continual decline in the global arable land base. The objectives of the study were to study the interaction between row spacing and N rate in oat (Avena sativa L.) on plant establishment and development, biomass production, grain quality, and grain yield under a no‐till production system. Four row spacing (25, 30, 35, and 40 cm) and five rates of N fertilizer were investigated for 3 yr. Plant density was not affected by N rate and there was no N rate by row spacing interaction. There was a 10% decrease in plant population going from 25 to 40 cm with some years showing no differences. Some differences on the origin and frequency of tillers were observed due to spacing. Grain yield was similar among 25, 30, and 35 cm row spacing with a 13% yield decrease at 40 cm. A row spacing by N rate interaction for grain yield was observed. Grain quality was not affected by spacing other than for a small increase in thin seed and seed weight at wider spacing. Grain N and P concentrations were not affected by row spacing. The results support the feasibility of wide row spacing up to 35 cm combined with placing all fertilizer requirements in a side‐banded position.
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 | 0.000 |
| 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".