Effect of sowing dates on different wheat varieties at Tikapur, Kailali, Nepal
Bibliographic record
Abstract
Growth and yield of wheat are affected by variety and sowing dates. Hence a field experiment was carried out at Agronomy Farm, Far Western University in Tikapur, Kailali, Nepal during 2023/24 to study the effect of sowing time and varieties on yield and yield attributing traits of wheat. The experiment was laid out in split plot design comprising four sowing dates viz. 17th November, 27th November, 7th December and 17th December as main plot and three varieties of wheat viz. Vijay, Gautam and Aaditya as sub plot, with three replications. Plant height, spike length, peduncle length, grain per spike, seed length, grain yield and straw yield were significantly higher when sown on 7th December for all the varieties. However, the highest number of effective tillers along with delayed heading, anthesis, maturity and lower grain filling period was observed on 17th November. Aaditya and Vijay produced higher grain yield on 7th December i.e. 4.93 t/ha and 4.25 t/ha, whereas Gautam yielded highest on 17th November with 4.45 t/ha. The last date of sowing produced the lowest grain yield across all the varieties. Similarly, the highest quantity of straw (6.72 t/ha) was observed in Gautam which was statistically at par with Aaditya, followed by Vijay on 7th December. Hence, 7th December for sowing Aaditya and Vijay under Tikapur condition owing to higher yields of both grain and straw whereas 17th November could be suggested for Gautam.
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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".