Potassium fertilization improves growth, yield and seed quality of sunflower (Helianthus annuus L.) under drought stress at different growth stages
Post-publication record
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Bibliographic record
Abstract
Water scarcity is a major concern for sunflower production in the semi-arid and arid regions of the world. Potassium (K) application has been found effective to alleviate the influence of drought stress; however, the impact of drought stress on seed quality of sunflower has not been reported frequently. Therefore, a field experiment was performed to determine the optimum K requirement for mitigating the adverse effects of water stress and improving growth and seed quality of spring-planted sunflower. Sunflower plants were exposed to water stress at different growth stages, i.e., Io = no stress (normal irrigation), I1 = pre-anthesisi stress (irrigation skipped at pre-anthesis stage), I2 = anthesis stress (irrigation skipped at anthesis stage) and I3 = post-anthesis stress (irrigation skipped at post-anthesis stage). Potassium was applied at four different rates, i.e., Ko = 0, K1 = 50, K2 = 100 and K3 = 150 kg ha-1. The results revealed that water stress at pre- and post-anthesis stages significantly reduced plant height, head diameter, number of achenes, oleic acid contents, and phosphorus (P) uptake. However, pre-anthesis stress improved linoleic acid contents. Treatment IoK3 (stress-free with 150 kg ha-1 K) was optimum combination for 1000-achene weight, biological and achene yields, oil contents, protein contents, and N and P uptake. Results indicated that a higher amount of K and irrigation resulted in higher yield, whereas yield and yield components decreased with early-stage water stress. Nevertheless, potassium application lowered the impacts of waters stress compared to no application. Keeping in view these results, it is recommended that sunflower must be supplied 150 kg ha-1 K in arid and semi-arid regions to achieve higher yield and better seed quality.
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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".