Combining Ability for Grain Yield Performance among CIMMYT Germplasm Adapted to the Mid-Altitude Conditions
Bibliographic record
Abstract
The International Centre for Maize and Wheat Improvement (CIMMYT) develops maize (Zea mays L.) inbred lines and hybrids yearly that have several breeding and commercial attributes. However, no genetic analysis has been done on the recently developed inbred lines for yield performance under drought and low-N stress. The objectives of this study were to identify lines with positive general combining ability (GCA) effects for grain yield under stress environments and to identify the best single-cross hybrids with the highest specific combining ability (SCA) effects. Analysis of variance combined across sites showed significant mean squares for genotypes, locations and genotype by environment interaction (GEI) for grain yield. GCAlines, SCA and components of interaction effects were significant across sites. Additive genetic variance was more important than dominance variance in determining yield performance across locations indicating that selection based on grain yield under drought and low-N stress can be effective. Average grain yield across the eight locations ranged from 1.61 t ha-1 to 10.63 t ha-1 while narrow sense heritability for grain yield was 52.6% across sites and was slightly lower under managed drought and low-N stress. The testers CL115807 and CL106622 showed positive and significant GCA effects for yield performance under drought and low-N stress respectively. The best tester across all sites was CL115793 and line CZL0713 had consistently positive GCA effects for grain yield across sites. CML536 × CL115802 and CML312 × CL106508 were the best single crosses under low nitrogen stress sites while hybrid CML312 × C323-45 showed the highest positive SCA effects across sites. In conclusion, our results show that CIMMYT has new lines that have desirable adaptive attributes when grown under drought and low nitrogen stress environments in the mid-altitude region; hence these can be adopted for hybrid, synthetic and OPV formation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".