Effect of zinc treatments and seed treatment on seed germination and seed health of barley
Bibliographic record
Abstract
In new agricultural systems, the use of seed treatment poisons and the simultaneous use of micro elements, especially zinc, has been widely used. This study was designed to investigate the reaction of this element to various common seed Treatment poisons and the effect of this action on germination and seed germination in two laboratory phases (2017, 2018) (completely randomized factorial design with four replications) and Field (randomized complete block factorial with four replications) on barley seeds of Goharan cultivar. Investigated factors included contamination of seeds with Loose Smut disease, hard Smut and barley brown spot, Seed Treatment Poisons (Dividend Star Liquid 2 per thousand, Carboxyine Tiram Liquid 2.5 per thousand Raxil, Liquid 0.5 per thousand, Lamardo Liquid was 0.2 per thousand, Reveral TS per thousand (and two types of zinc treatment (ZN Cavin produced by Cavine company and Zn Zagort produced by Pars Forough Zagros company). The diseased plants were collected from the field and the seeds were subjected to washing, embryonic and osmotic tests to detect hard Smut, Loos Smut and barley brown spots as well as standard germination tests and germination-related traits. Field results showed that the recommended fungicides controlled well disease of Loose Smut and hard Smut. In barley brown spot disease, the highest level of disease control was observed in seeds Treatment with Revral TS and Carboxin Tiram. Disease seen in non- treatments Seeds had little effect on barley-brown barley spot and Loose Smut disease. Seed Treatment is the only method of controlling seed-borne diseases such as striped brown spot disease and Loose Smut barley. The results of in vitro tests and analysis of variance showed that Lamardo and carboxin tiram toxins were the best options for controlling Loose Smut disease. All the toxins except Reveral TS were able to control the hard barley disease and carboxin tiram was the best option. In the case of barley brown stains, Reveral TS was the most effective and the others had poorer control. The results also showed that none of the treatments had significant effect on seedling emergence index and seedling emergence rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".