Maize Yield Potential and Density Tolerance
Bibliographic record
Abstract
Maize ( Zea mays L.) yield potential has not undergone genetic improvement during the hybrid era, yet substantial genetic improvement has occurred for tolerance to high plant population densities. As many crops including maize are approaching yield plateaus, it may be necessary to exploit other means of increasing grain yields. In this study, we examine potential reasons for why this occurred. Using a four‐way breeding cross representing the commercial germplasm pool, we demonstrate that the lack of genetic improvement in yield potential is not due to the two attributes being antagonistic. We then demonstrate that the lack of genetic improvement in yield potential is not due to density‐tolerant genotypes being higher yielding at modern conventional plant densities. We show that physiological differences in partitioning dry matter to the grain (i.e., harvest index [HI]) are present in a set of genotypes with contrasting yield potential and density tolerance genotypes. However, a higher or a lower HI is not associated with yield potential either. Finally, we show that the density‐tolerant genotypes exhibit a static kernel set efficiency (KSE), meaning that regardless of the plant growth rate at silking (pGR S ), the number of kernels formed per unit dry matter fixed is constant. Surprisingly, the hybrids with high yield potential possess a dynamic KSE and are capable of sustaining kernel set at higher levels when pGR S is low. Given our findings, there is no apparent biological or genetic explanation for genetic improvement in only density tolerance during the hybrid era.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".