Study on reduction of barley seed consumption in the farmer conditions in west azerbaijan province of Iran
Bibliographic record
Abstract
Seed rate is less or more than usual and for some reasons such as big size seed cultivars, with low-tiller potential cultivars, spring cultivation, late cultivation, spraying method, heavy soils, germination percentage Low and, etc. are used by farmers. Also, the effect of relative fluctuations of atmospheric parameters on seed vitality, germination and other seed characteristics reduce plant establishment at planting. For this reason, the amount of seed consumed per hectare by farmers is more than required in barley farming and sometimes it is observed that the seed consumption per hectare is more than 250 kg and in parts of the country up to 300 kg/ha. In this study, In addition to investigating the possibility of reducing barley seed consumption under field cultivation conditions, as the project is being implemented in agricultural lands, it will promote the reduction of seed consumption by farmers themselves. The study was conducted in the fall of 2016–2017 and 2017–2018 at the same time as barley cultivation. The method of application was that in the field of seed propagation contractor farmers, different amounts (50, 100, 150, 200 and 250 kg/ha) of modified barley seed identified, Reyhan 03 at the level of each barley seed per 1000 m 2 The form was cultivated separately by the common method of the region. Green plant percentage (after complete plant establishment), percentage and number of tillers (after tillering), yield components including number of spike per m2, number of fertile spike, 1000-seed weight, number of grain per spike and grain yield per unit area (At harvest) were evaluated. The results showed that the highest grain yield was obtained in West Azarbaijan province (Shahin Dezh city) at 150 kg/ha. In this province, high seed consumption not only increased grain yield but also increased production costs. According to the results of this study, proper agronomic management can reduce the high seed consumption.
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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 | 0.001 | 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".