Analysis of Genotype by Environment Interaction of Improved Pearl Millet for Grain Yield and Rust Resistance
Bibliographic record
Abstract
Pearl millet is grown by inhabitants of the semi-arid zones. Due to the unpredictable climatic conditions the genotype-by-environment interaction (GEI) makes it hard to select genotypes adapted to such conditions. The study objectives therefore were to analyse the patterns of GEI and to identify superior genotypes for grain yield and rust resistance. Seventy six genotypes were planted in four environments in 4×19 alpha design with two replications. The ANOVA results showed that main effects of environments were significant (p ≤ 0.05) for grain yield and highly significant (p ≤ 0.001) for rust resistance while the main effects of the genotypes and their interactions with environments were also important for grain yield and rust severity at 50% physiological maturity. The GGE biplot analysis revealed that environments associated with more rains received during vegetative phase performed better than those receiving more rains during post-anthesis phase. The winner in the best environment for grain yield was ICMV3771×SDMV96053 while Shibe×CIVT9206 and Shibe×GGB8735 were the best for rust resistance.
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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 | 0.000 | 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) | 0.000 | 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".