The Effect of Matrix Nutrition by Sulfonated Silicon With S8 on Tuber Yield and Its Components of Potato Cultivars Under Water Deficit Stress Condition
Bibliographic record
Abstract
The goal of this study is to increase the tuber yield of potato cultivars by using of nutrient solution of sulfonated silicon with S8 sulfur element and to choose the most suitable method of using the nutrient solution under water deficit stress and normal conditions. This study was performed based on a split factorial experimental design in three replications in Ardabil Potato Research Station, IRAN in 2022. The main factor includes three levels of irrigation (100, 75, and 50% of plant usable water); the second factor includes foliar spraying with a nutrient solution in four stages of plant growth [(1) Tuber formation; (2) Tuber bulking; (3) Tuber formation and tuber bulking; (4) Control (Without nutritional solution)] and the third factor included three potato varieties (Agria, Rona and Takta). A nutrient solution of Sulfonated silicon with S8sulfur element was used for 5 liters per thousand of water. During the growth period, plant height, main stem number per plant, tuber number and weight per plant, tuber yield and water use efficiency were measured. The results showed that foliar spraying with a nutrient solution of sulfonated silicon with S-8 sulfur element amount 5 liters per thousand of water in the stages of tuber formation and tuber bulking increased tuber yield and water use efficiency under normal conditions (100% of plant usable water) about 14.24 ton per hectare and 2.44 kg/m3, under mild stress condition (75% of plant usable water) about 7.04 ton per hectare and 1.61 kg/m3 and under severe stress condition (50% of plant usable water) about 5.50 ton per hectare and 1.89 kg/m3, respectively. The use of the nutrient solution of sulfonated silicon with S8 sulfur element amount 5liters per thousand of water in the stages of tuber formation and tuber bulking increased tuber yield and its components and water use efficiency under normal, mild and severe conditions.
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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".