Responses of wheat yield, quality and bread-making properties on the sulphur fertilization
Bibliographic record
Abstract
This study presents a versatile influence of sulphur (S) application on winter wheat – beginning with the influence on the grain yield and primary quality indices, followed by the one on the bread-making properties of flour and dough, and ending with the one on the quality indices of baked products. Field experiments were conducted at two locations on haplic Luvisol and calcaric Cambisol during five years. S was given as NS-fertilizers on the background of N100 during wheat tillering. Flour and dough properties for bread-making were determined by using a farinograph Brabender. The baking tests were carried out in laboratory conditions. The influence of S on the grain yield and quality, and bread-making properties of wheat depended on the year and location. As the average of field experiments conducted at two locations, the application of S significantly increased the grain yield (r = 0.960). The increasing yields were accompanied by decreasing contents of wet gluten (r = 0.825). However, the significant positive (r = 0.938) effect of the S application on the Gluten index was revealed, which is a good predictor of the baking quality of wheat flour. The S addition by nitrogen fertilization resulted also in a positive shift of the N:S ratio in grains, which is better for the bread-making quality. The significant positive effects of the S application on the dough stability time (r = 0.898) and on the farinograph quality number (r = 0.917) were demonstrated. The loaf volume was significantly (r = 0.842) increasing under the influence of S. Thus, the application of S in parallel with increasing yields improved several bread-making parameters of wheat.
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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".