Physiological Basis of Heterosis for Grain Yield in Maize
Bibliographic record
Abstract
Although heterosis in maize ( Zea mays L.) has been studied since the early 1900s, very little is known about how heterosis affects the physiological components of grain yield. The objective of this study was to quantify the physiological basis of heterosis for grain yield in maize by examining maize hybrids and their parental inbred lines in terms of grain yield and its component processes, dry matter accumulation (DMA) at maturity, and the partitioning of DMA to the grain (i.e., harvest index), as well as in terms of the physiological processes underlying those two components. The genetic material consisted of 12 maize hybrids and seven parental inbred lines. Experiments were conducted from 2000 to 2002 at the Elora Research Station, ON, Canada. Data were recorded on grain yield, DMA at four stages of development, harvest index, leaf area index (LAI), final leaf number, leaf width and length, rate of leaf appearance, stay green, ear number, kernel number and weight, and number of days to silking and physiological maturity. Mean heterosis across the 3 yr was 167% for grain yield and 85 and 53% for its two component processes, DMA at maturity and harvest index, respectively. Results show that heterosis for grain yield in maize can be attributed to (i) heterosis for DMA before silking, which results mainly from greater light interception due to increased leaf size; (ii) heterosis for DMA during the grain‐filling period, which results from greater light interception due to greater maximum LAI and increased stay green, and (iii) heterosis for harvest index.
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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 | 0.000 |
| 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 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".