Interaction of Phosphorus and Potassium on Maize (Zea mays L.) in Saline-Sodic Soil
Bibliographic record
Abstract
Salinity and nutrient deficiencies are the main constraints for high crop productivity. Interaction of diammonium phosphate and potassium sulphate in saline-sodic soil for maize (Zea mays L.) crop was investigated. The results demonstrated that maize responded well to K and P fertilization in saline-sodic soils. The effects of salinity and sodicity were ameliorated by the application of K and P fertilizers resulting in higher yield. K had greater influence on grain yield than P level. K application increased yield related parameters. The addition of P significant affected leaf [P] and [Na] content, Na:K and Ca:Na ratios. Potassium levels had significant effects on [Na], [K], [Mg] and Na:K ratio. Phosphorus and K interactions did not affect leaf chemical composition except Mg content. The P application resulted in an increase of [P] in maize leaf tissue as compared to control. A decrease in [Na] and Na:K ratio was observed with the addition of K. There was positive relationship between grain yield (R2 = 0.67), dry matter yield (R2 = 0.76) and leaf [P], respectively in soils treated with P. The tissue [Ca], ratios of Ca:K and Ca:P were non-significantly affected by the K and P treatments. Extractable [P] increased after P treatments in the soil. The application of K significantly decreased Na:K ratios in the soil. The decreasing trends of [Na] and Na:K ratios depicted a negative (R2 = 0.91) correlation between Na:K and soil [K]. Such interaction of K and P could mitigate the adverse effects of salinity and sodicity.
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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".