Effect of Cotton Varieties and Plant Spacing on Yield and Yield Components of Compact Type Cotton Under HDPS.
Bibliographic record
Abstract
To improve productivity and profitability, it is important to investigate new strategies for cotton production. In April 2020, a field experiment was conducted to study the effect of different plant varieties and spacing on the growth and yield of compact-type cotton. The experiment followed a split-plot design with three replications, with four levels of cotton varieties (Suraj, Supriya, CO 15 and CO 17) in the main plots and seven levels of plant spacing (60 cm x 10 cm, 60 cm x 15 cm, 80 cm x 10 cm, 80 cm x 15 cm, 100 cm x 10 cm, 100 cm x 15 cm, and 75 cm x 30 cm) in the subplots. The results revealed that cotton variety CO 17 had been recorded significantly the highest yield and yield attributes, highest number of sympodial branches per plant and number of squares per plant counted higher when compared to other varieties. However, relative parameters the significantly highest number of bolls per plant counted and higher boll weight was recorded and maximum seed cotton yield harvested with CO 17 variety than others. Results revealed that the 100 x 10 cm plant spacing recorded the highest yield contributing characteristics, such as number of sympodial branches per plant, number of squares per plant and number of bolls per plant. Closer spacing has recorded a significantly higher number of bolls per plant; the highest single boll weight and maximum seed cotton yield were recorded. Among the interaction varieties, CO 17 with a 100 x 10 cm plant spacing registered higher number of sympodial branches per plant, number of squares per plant and number of bolls per plant for yield parameters, comparing to the yield CO 17 with 100 x 10 cm recorded higher seed cotton yield, higher number of bolls per plant and boll weight.
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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".