Genetic Variability and AMMI Bi-Plot Analysis in Bread Wheat Based on Multi-location Trials Conducted under Drought Conditions across Agro-climatic Zones of India
Bibliographic record
Abstract
Wheat grown globally under different agro-ecological conditions and drought conditions has emerged as a priority research area for developing genotypes in the near future. In this research, 36 genotypes, including checks (HW 2004, HD 2888, PBW 175 and NI 5439) were tested at 12 locations covering four mega wheat growing zones of India, followed by augmented design under drought conditions, during the crop season 2012/13. The main objective of this research was: to estimate variability parameters and correlation; determining the closeness of experimental sites; and identification of site specific adapted wheat genotypes. Our research findings revealed higher broad-sense heritability for plant height (89-98%) and thousand grain weight (80-93%) in all four zones of India. Herein positive and significant phenotypic correlation was found between thousand grain weight and grain yield; while days to heading, days to maturity and plant height were negatively correlated with grain yield under peninsular zone comparable to other zones. Based upon mean yield across locations, the genotypes MACS 6348 (25 q/ha); HD 3043 and AKAW 4635 (24 q/ha each) were found better than others. AMMI analysis revealed that locations; Kota, Sagar, Indore and Niphad were very close to each other and five genotypes were found to be stable in these locations. The locations; Ranchi, Delhi, Gurdaspur and Pune had also been very close to each other and we have identified seven stable genotypes for these locations. Our findings would be very spectacular for wheat breeders conducting multi-location trials. Herein; the promising genotypes identified for different locations could serve as donors to develop the multi-parent advanced generation integrated cross populations to stack genes/alleles conferring drought tolerance.
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) | 0.001 | 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) | 0.000 | 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".