Response of Two Wheat Cultivars to Supplemental Nitrogen under Different Salinity Stress
Bibliographic record
Abstract
Effects of supplemental nitrogen (N), as either farmyard manure (FYM) or urea, on response of two wheat (Triticum aestivum) cultivars (a salt sensitive ‘Sakha 69’ and a salt tolerant ‘Sakha 93’) were investigated in a green house experiment under various salinity levels (control, 6, 9, or 12 dS m-1). Grain and straw yields of both cultivars decreased with an increase in salinity levels. Supplamental N application, using FYM or urea, mitigated the adverse effects of salinity only at the low salinity level (6 dS m-1). This effect was greater in a salt tolerant cultivar (Sakha 93) than that in a salt sensitive cultivar (Sakha 69). At the moderate and high salinity (9 and 12 dS m-1) levels the supplemental N had no beneficial effects in mitigating the salinity stress of both cultivars. The mean grain yields, across all salinity levels and cultivars, of the plants received FYM and urea were greater by 11, and 8%, respectively, as compared to that of the plants received no supplemental N. The corresponding values for straw were 12 and 7%. The concentrations of N, P and K in the grain and straw significantly decreased with increasing salinity levels. Concentrations of Na, Cl, and Ca in the grain and straw were greater in salt sensitive cultivar than those in a salt tolerant cultivar. Concentrations of these elements significantly increased with an increase in salinity levels. This study demonstrated that supplemental N, as either FYM or as Urea, can mitigate negative effects of mild salinity stress, and that this beneficial effect was greater in a salt tolerant cultivar as compared to that in a salt sensitive cultivar.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".