Grain Yield Performance and Stability of Quality Protein Maize Single Cross Hybrids in Mid-altitude Environment in Uganda
Bibliographic record
Abstract
Stability in performance is important for determining adaptation and recommendation of pre-commercial crop varieties. This study was conducted with the following objectives: i) to determine stability of grain yield for 55 quality protein maize (QPM) single cross hybrids generated from 14 inbred lines ii) to determine the pattern of grouping of QPM hybrids and test environments based on grain yield response. The test hybrids were generated during the second season of 2015 and evaluated in three agro-ecological zones during the first season of 2016. Two checks were used: Longe 5D, a popular QPM hybrid and a top cross of Longe 5D with CML511. Additive main effects and multiplicative interaction (AMMI) and genotype and genotype by environment interaction (GGE) analyses were used to assess the stability of the hybrids. Results showed highly significant differences between genotypes, environments and GEI. The first principal component axis (IPCAI) was significant (p < 0.01) and accounted for 61.5% of the interaction effect. Both (IPCAI) and IPCAII) cumulatively contributed to entire degrees of freedom available for interaction component. Hybrid QPMSC-29 had the highest grain yield across environments. The AMMI biplot clearly depicted the genotypes on the bases of their adaptation patterns. Hybrids QPMSC-43, QPMSC-12, QPMSC-18 and QPMSC-29 were found to be more stable and responsive to favorable environments. Among them QPMSC-18 was more stable across locations. The AMMI biplot successfully identified 2 mega-environments as Namulonge and Bulindi in the first mega-environment with QPMSC-29 as the winning genotype and Masaka as the second mega-environment with QPMSC-10 as the winning genotypes. Hybrid, QPMSC-46 was an ideal genotype with above average score for grain yield. The single cross hybrids QPMSC-29, QPMSC-18 and QPMSC-10 were identified as stable yielder across environments in addition to higher yield. These hybrids can be recommended for all the three locations, for cultivation.
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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 | 0.002 | 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".