Evaluation of Sulfur and Nitrogen Utilization on Agronomic Traits and Fatty Acid Profiles of Safflower Using a Tester Biplot Model
Bibliographic record
Abstract
This study investigated the effects of different sulfur fertilizers and nitrogen levels on the agronomic performance and fatty acid profile of safflower. The experimental treatments included sulfur application at varying rates and sources: S0 (no sulfur application), S25 (25 kg ha-1 sulfur from elemental sulfur, ES), S50 (50 kg ha-1 sulfur from ES), ZS25 (25 kg ha-1 sulfur from zinc sulfate, ZS), and ZS50 (50 kg ha-1 sulfur from ZS). Nitrogen was applied at three levels: N0, N40, and N80 (0, 40, and 80 kg ha-1 nitrogen from urea fertilizer). The experiment was conducted in Baneh, Iran, in 2021. The entry-by-tester (treatment-by-trait) biplot analysis, which accounted for 80% of the observed variability, identified the N80-ZS50 treatment as the most effective in enhancing key traits, including yield, oil content, and specific fatty acids such as linolenic acid. Additionally, other unsaturated fatty acids, including oleic, linoleic, and arachidic acids, exhibited higher concentrations under the N0-S50 treatment. A positive correlation was observed between fatty acid composition, oil content, and protein, as well as among various agronomic traits. Based on overall performance and trait differentiation, N80-ZS50 emerged as the optimal treatment, followed by N80-S25. Trait discriminative analysis highlighted stearic acid, oil content, and linolenic acid as key determinants in safflower evaluation. These findings underscore the significant influence of sulfur and nitrogen fertilization on safflower characteristics, demonstrating the benefits of their combined application. The N80-ZS50 treatment (80 kg ha-1 nitrogen and 50 kg ha-1 sulfur from zinc sulfate) is recommended to enhance safflower performance in upland semi-arid regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".