Differences in Wheat Cultivar Response to N Supply. I: Differences in Grain Yield Formation
Bibliographic record
Abstract
Abstract Two‐year field trials with winter wheat cultivars Batis and Toronto were conducted in Southern Bavaria, Germany, to investigate the possible causes of cultivar differences in response to N supply varying in total amount and time of application. Cultivar‐related differences in grain yields were observed in treatments with low and medium N supply. High doses of N supply resulted in grain yield adjustment or grain yield advantage for cv. Toronto. The results of this study revealed a consistent, genotypic pattern in response to N fertilization in spite of strong seasonal effects. Systematic modifications in canopy growth rates in response to N supply were of particular relevance and a main factor for differences in tillering intensity resulting in modified stand densities. In the present study, cultivar differences in spike development and interactions with N supply related more to abortion than to initiation processes for number of spikelets and number of flowers per spikelet. High grain density (grains per m2) of cv. Toronto was evident during reproduction stages even under conditions of medium N supply. However, decreased growth rates during the later part of grain filling in combination with low 1000 grain weight, which was barely modified by N fertilization, allowed only partial utilization of this potential. It is assumed that sink limitations were of particular relevance for grain yield development in cv. Toronto, while cv. Batis combined a less intense response to N supply with more stability in the development of grain yield components.
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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".