Genotype × Environment Interaction of Quality Protein Maize Hybrids under Contrasting Management Conditions in Eastern and Southern Africa
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Drought and low soil fertility are major abiotic stresses limiting yield of maize ( Zea mays L.) in eastern and southern Africa. The present study was undertaken to determine genotype by environment interaction (GEI) and grain yield stability of quality protein maize (QPM) experimental hybrids. A total of 108 hybrids, including two commercial checks, were tested across 13 environments under drought, low N, and optimal environments in Ethiopia, Zambia, and Zimbabwe in 2015 and 2016. Environment, hybrid, and hybrid × environment interaction effects were significant ( P < 0.01) across environments and within management conditions. The highest yielding hybrids were H40, H41, H56, and H58 under optimum management; H2, H9, H40, and H87 under low N; H3, H10, H11, and H94 under drought; and H9, H10, H40, H56, and H94 across environments. The GEI and grain yield stability analysis using different models indicated that additive main effects and multiplicative interaction (AMMI), and genotypic main effects plus GEI (GGE) models were more efficient and precise compared to the linear regression stability model in identifying high‐yielding hybrids with stable performance. Based on the AMMI and GGE biplots, the most promising QPM hybrids were identified under different management conditions. Hybrid H40 was the most outstanding genotype under various management conditions and could be used in breeding programs or commercialized in target areas. Gwebi optimum and Bako low N were identified as the most discriminating and representative environments under the contrasting management conditions. In general, results of the present study depicted the possibility of developing high‐yielding and stable QPM hybrids for stress and nonstress conditions.
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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 it