Salinity Tolerant Indices Based on Yield Performance of Some Sugar Beet Varieties as Treated by Potassium Silicate to Mitigate Saline Soil Stress
Bibliographic record
Abstract
Salinity stress is a significant abiotic factor that limits the yield and quality of sugar beet grown in newly reclaimed saline lands. The field experiment was conducted at a private farm in Tamia (29° 17ˋ N, 30° 53ˋ E), Fayoum, Egypt, in 2021/2022 and 2022/2023 successive seasons. The objective of this study was to assess the usefulness of potassium silicate (K2SiO3) in four K-silicate foliar spray concentrations in alleviating salinity stress on five sugar beet varieties grown in saline soil. A split-plot design in a randomized complete block arrangement was used with three replications. Spraying K-silicate showed improved sugar beet varieties' tolerance to soil salinity. Increasing the concentration of the sprayed K-silicate gave higher root and sugar yield productivity. Results showed that the varieties significantly differed, where the Narmar and Afendra varieties showed superiority over the other three tested varieties, with the highest values of root and sugar yield and it is related traits in both seasons. The potassium silicate rate of 2,000 mg/L gave the highest juice quality and lowest impurities, suggesting a great potential for using potassium silicates with sugar beet to produce high roots and quality for economical sugar production under saline soil. The sugar beet varieties with less than one unit of salinity tolerance index (STI), yield stability index (YSI), and salinity susceptibility index (SSI) values were suitable for cultivation under saline soil stress and non-stress environments. These indices were more effective in identifying high-yielding varieties under saline soil stress as well as non-stress 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) | 0.000 | 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 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".