Multivariate Analysis of Elite <i>Bt </i>Cotton Genotypes for Seed Cotton Yield and Fiber Quality Traits Under Semi-Arid Conditions
Bibliographic record
Abstract
Cotton production in semi-arid regions like Pakistan is constrained by water scarcity and heat stress, resulting in yields significantly lower than global leaders like China. This study evaluated ten elite Bt cotton genotypes (BH-318, BH-348, BH-224, BH-563, BH-405, BH-410, BH-606, BH-248, BH-184, CIM-600) for yield, fiber quality, and associated traits under semi-arid conditions at the Cotton Research Station, Bahawalpur, using a Randomized Complete Block Design with three replications. ANOVA confirmed significant genotypic variation (p ≤ 0.01) for plant height, nodes, sympodial branches, photosynthetic rate, fiber strength, and yield, with BH-348 and BH-224 achieving the maximum yields (2169.1 kg/ha in Cluster 1). Correlation analysis revealed positive associations between yield and nodes (r = 0.741**), plant height (r = 0.548), and sympodial branches (r = 0.416), while PCA showed PC1 (29.4%) and PC2 (22.6%) explaining 52% of the variation, driven by yield and fiber quality traits, respectively. Cluster analysis grouped genotypes into two clusters, with Cluster-1 (BH-318, BH-348, BH-224, BH-606, BH-410, BH-248) excelling in yield and Cluster-2 (BH-563, BH-405, BH-184, CIM-600) in fiber quality (fiber length: 28.9 mm, fiber strength: 35.4 g/tex). The findings from the present multivariate analyses highlight BH-348 and BH-224 as ideal for yield-focused breeding and BH-563 for quality improvement, offering strategies to enhance cotton productivity and quality in semi-arid environments.
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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 | 0.001 |
| 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".