Response of Wheat Crop to Potassium Fertilization Under Rain-fed Conditions in Semi-arid Regions
Bibliographic record
Abstract
Wheat is regarded as one of the major field crops that is mainly grown under rain-fed conditions in Jordan. However, wheat productivity is relatively lower than the world average. This is primarily due to the prevailing drought conditions as a result of poor distribution and low amounts of rainfall. Research studies showed that K can markedly improve wheat yield and quality under water deficit conditions. The objective of the current study was to investigate the effect of soil-applied mineral potassium (K) fertilizer on the yield and growth parameters of two local durum wheat varieties under rain-fed conditions in Jordan. And to determine the rate of K needed to obtain the optimum wheat yield. Two field trials were conducted under rain-fed conditions in two locations in Jordan. Two local durum wheat varieties were grown during two successive growing seasons. Five different rates of K were applied at sowing time. A randomized complete block design with four replications was followed. The results showed that soil K application exhibited a significant effect on wheat crop grain and biological yields of both varieties at the two locations. However, an increasing trend in plant height, harvest index, and thousand-grain weight with increasing K application rate was noticed. Potassium application to soil can alleviate the adverse effects of drought stress on the wheat crop by improving growth and yield attributes.
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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".