Quantitative Genetic Analysis of the Physiological Processes underlying Maize Grain Yield
Bibliographic record
Abstract
Few studies have examined the inheritance and interrelationships of both grain yield and the underlying physiological processes in maize (Zea mays L.). The objective of this study was to establish genetic relationships between the physiological components of grain yield and to examine the inheritance of grain yield and its component processes (i.e., additive and the nonadditive genetic effects). Twelve F1 hybrids, obtained by mating three male and four female inbred lines using a North Carolina Design II, were evaluated in trials conducted in Ontario from 2000 to 2002. Dry matter accumulation (DMA) at four stages of development, harvest index, leaf area index (LAI), stay green, and grain yield were measured. Variation among the 12 hybrids was significant for all traits evaluated, and the range in mean grain yield was 28% of the mean. Using the genetic effects partitioned by a Design II analysis, we dissected the physiological mechanisms that influenced favorable or unfavorable contributions to grain yield. Using the highest‐ and lowest‐yielding hybrids in the study (i.e., maximum genetic variation), we attempted to dissect the physiological reasons for the difference in grain yield. This analysis, however, was unsuccessful in dissecting grain yield in terms of physiological mechanisms using a quantitative genetic model. Reasons for this failure may be, in part, (i) the relatively low contribution of statistically significant genetic effects to the differences between the hybrids; and (ii) partitioning of the difference between hybrids in four general combining ability (GCA) estimates and two specific combining ability (SCA) estimates results in small estimates relative to the grand mean.
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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 | 0.000 | 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 | 0.000 | 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 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".