Multivariate Analysis for Yield and Its Component Traits in Experimental Maize Hybrids
Bibliographic record
Abstract
Crop yields of major cereal including maize are not increasing at the targeted growth rates to feed the rising demands stemming from increase in the human population. To increase maize grain yield, there should be continuous improvement of cultures which are actively utilized by the plant breeders. Variability in germplasm is always the key to improvement and to assess the extent of variation is never ending process in a plant breeding program. Out of several methods available for assessing the variability, multivariate analysis is one of the most important and widely used methods. In the present study, 27 hybrids (including three checks) were evaluated for yield and yield contributing traits at three different locations during rabi 2013-14. Analysis of variance revealed significant variations among hybrids for all the traits. Based on Principal Component Analysis, 76.81% of the total variance in the data was accounted for by first four principal components (PC). Cluster analysis based on PC grouped the 27 hybrids into two major groups named as A and B. The group A further contained three sub-groups named as A1, A2, and A3 with two hybrids falling in each group. Similarly group B contained four subgroups classified as B1 to B4 with 2, 7, 5 and 7 hybrids falling in each subgroup respectively. The hybrids falling in two major groups contained more diversity than those falling in subgroups within a group. Selection of hybrids from the different groups would facilitate exploiting significant heterosis. Therefore, multivariate analysis including Principal component analysis followed by cluster analysis could be a reliable approach for assessing the extent of variability on in the germplasm and making its use in a suitable direction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".