Determining the Optimal Plant Spacing for Cotton Varieties to Enhance the Growth and Yield of Cotton Under HDPS in Karaikal Region.
Bibliographic record
Abstract
Cotton has now become well-known and grown all throughout the world and all the states of India. India's seed cotton production per unit area is still significantly lower than that of several other cotton-growing countries throughout the world. Two of the most prominent factors leading to the country's low cotton crop productivity are a lack of plant population and the use of low-potential cultivars. 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 crop growth such as plant height, leaf area index and dry matter production, was higher in CO 17 than in other varieties. Compared to the yield and yield component variety CO 17 recorded higher seed cotton yield with more bolls per plant and highest boll weight than others. Results revealed that the higher plant spacing of 1,66,666 plants (60 x 10 cm) observed significant maximum plant height. In contrast, the 100 x 10 cm plant spacing recorded the highest growth contributing characteristics, such as leaf area index and dry matter production. 100 x 10 cm 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 the highest leaf area index and dry matter production in growth, considering yield and yield parameters, recorded higher seed cotton yield, higher number of bolls per plant and boll weight during the cropping period.
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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".