Evaluation of Triticum aestivum L. germplasm against Puccinia striiformis and its management through botanicals
Bibliographic record
Abstract
Wheat is the staple food of Pakistan and country facing wheat shortage during recent years leading to food security issue. Rust diseases of wheat are significantly important causing major dent in wheat production during last season’s due to drastic climate change. Therefore, wheat germplasm was screened against wheat stripe rust during two consecutive years. Out of one hundred and five genotypes not even one showed immune response during 2018-19, 22 genotypes showed highly resistant response, 19 showed resistance response, 42 showed moderately resistance response and remaining genotypes showed susceptible response except five lines (CB-10, CB-65, CB-95, CB-84 and CB-31) that showed heterogeneous characters. Likewise, during 2019-20, 18 genotypes showed highly resistant response, 23 showed resistant response, 39 were moderately resistance and remaining genotypes showed susceptible response except four lines (CB-10, CB-65, CB-95 and CB-84) that showed heterogeneous response. For each year value of area under disease progress curve (AUDPC) of all genotypes was also calculated which falls between 100-850. Efficacy of four plant extracts (neem, garlic, ginger and bell pepper) using seed soaking method in controlling the stripe rust disease of wheat was investigated in pots experiment. During both years, minimum disease was observed in case of garlic bulb extract followed by neem leaves extract. Ginger bulb and Bell pepper fruit extract also had significant effect against wheat stripe rust. From the current study it could be suggested that using highly resistant germplasm advance lines may be developed that exhibit the resistant genes against stripe rust pathogen and it is observed that instead if using fungicides, use of botanicals not only reduced the human health hazard but also control the disease effectively.
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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 | 0.001 |
| 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".