Crop Nitrogen Demand and Grain Protein Concentration of Spring and Winter Wheat
Bibliographic record
Abstract
Available soil N and a cultivar's genetic potential are primary factors determining grain protein concentration (GPC). This study focused on important genotypic and environmental factors that determine GPC and yield potential in common wheat (Triticum aestivum L.) and investigated the use of GPC as a practical indicator of crop N deficiencies for a wide range of cultivars grown in 16 N fertilizer trials in western Canada. Large GPC responses to added N were accompanied by large increases in grain yield, and similar GPC–grain yield relationships were found at maximum grain yield and 90 and 80% of maximum grain yield. Both genotype and environment influenced the upper limit of yield when N was not limiting. The relationship between GPC and grain yield depended on the part of the N fertilizer response curve sampled, and there was a strong negative correlation between cultivar GPC and maximum potential grain yield. The latter observation indicates that the production of high‐yielding cultivars with high GPC is more complicated than simply stacking yield genes in a high‐GPC genetic background or vice versa. Large differences amongst cultivars also suggested that the critical GPC–grain yield responses must be known for each cultivar before GPC can be used as a practical postharvest indicator of N sufficiency. Growing season weather had a large influence on GPC–grain yield relationships, and GPC at the point of maximum grain yield increased as the potential grain yield of a cultivar was reduced by environmental limitations. These observations indicate that GPC may be a useful postharvest indicator of N deficiencies for crops that are under N stress, but caution must be used when employing GPC to develop management systems that optimize N fertilizer use.
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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 | 0.000 |
| 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".