Spring Wheat Yield and Quality Related to Soil Texture and Nitrogen Fertilization
Bibliographic record
Abstract
Efficient N fertilization is crucial for economic wheat (Triticum aestivum L.) production and is of great agronomical and environmental significance. A study was conducted at 12 site‐years in eastern Canada to evaluate the effect of soil surface textural groups, N rate (0–200 kg N ha−1) and application timing on grain yield (GY), N uptake, nitrogen uptake efficiency (NUE), grain protein content (GPC), test weight, and thousand kernel weight (TKW). Chlorophyll meter readings (CMR) were taken at tillering and at flowering to assess in‐season wheat N nutrition. Fertilization and soil textural group effects were significant on all measured parameters and their interaction was significant on GPC, TKW, test weight, and CMR. Total N uptake and GPC ranged from 39 to 96 kg N ha−1 and from 13 to 18 g kg−1, respectively, and total N uptake increased proportionally to N rates. Applying N levels >120 kg N ha−1 did not increase total yield, test weight, TKW, or CMR values. The variation in GY, N uptake, and GPC explained by the relative CMR taken at flowering was 87, 88, and 73%, respectively. This study demonstrates that in‐season wheat N nutrition can be monitored by CMR and that surface soil texture is an important parameter that influences wheat N response and wheat quality parameters. Applying half of the recommended rate (120 kg ha−1) at planting and the rest at tillering resulted in a high total yield, high grain N uptake, and the highest GPC price premium.
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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 | 0.000 | 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 | 0.000 | 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".