SELECTION OF STABLE WHEAT GENOTYPES UNDER DIFFERENT AGRO-ECOLOGICAL ZONES OF PUNJAB, PAKISTAN
Bibliographic record
Abstract
Wheat is a globally significant cereal crop crucial for ensuring food security. Plant breeders strive to enhance yield potential by developing optimized and stable genotypes. In Pakistan, an agricultural country facing food security challenges, annual multi-environment trials (MET)’s systematic conduct transpire across various research stations in Punjab province. Precise data analysis of these trials is paramount in strengthening the national agricultural research system. The primary objective of this study was to identify stable wheat genotypes by analyzing data from MET trials in 31 distinct environments within the Punjab province during 2020–2021. The study comprised 50 wheat genotypes laid out under an alpha lattice design. The collected data underwent an analysis based on additive main effects and multiplicative interaction (AMMI) in combination with other stability measures. The findings revealed that genotype G41 (HYT100-27) exhibited superior performance, ranking within the top five across all five stability measures. Likewise, G27 (TWS17042) and G22 (HYT100-100) genotypes have four stability measures recommending these. Notably, G1 (HYT100-74) demonstrated the highest average yield across all locations and gained support from two additional stability measures. Therefore, G41, G27, G22, and G1 emerged as the most stable and productive genotypes among all those studied. Regarding the environments, MLSI proved the most desirable, followed by RARL. Conversely, the ARFG and ARFK resulted as the least ideal 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".