Physiological Basis of Successful Breeding Strategies for Maize Grain Yield
Bibliographic record
Abstract
ABSTRACT During the maize (Zea mays L.) hybrid era (1939 to present), commercial grain yields have improved nearly sixfold and the genetic component of the improvement has been estimated as approximately 60%. In this paper, we examine physiological factors and successful breeding strategies that underlie the yield improvement. Grain yield is the product of accumulating dry matter and allocating a portion of the total dry matter to the grain. The processes influencing dry matter accumulation are commonly referred to as the “source” components, while the processes influencing allocation of dry matter to the grain are referred to as the “sink” components. On the source side, changes in leaf canopy size and architecture account for only a minor portion of the improvement. The majority of the improvement in source capacity is due to visual and functional “stay‐green.” On the sink side, the improvement is through changes in the relationship between kernel number per plant and plant growth rate during a period bracketing silking. In a breeding context, these improvements have been made (i) in a “closed” germplasm pool stratified into heterotic groups; (ii) through use of a pedigree method of breeding structured to mimic reciprocal recurrent selection and thereby improving both additive and nonadditive genetic effects; and (iii) by a gradual increase in plant population densities during the hybrid era as the constant source of stress during both inbred line development and hybrid commercialization. Functional stay‐green and the sink establishment dynamics still represent opportunities for yield improvements. It is essential that source and sink are kept in balance, and that improvement in one accompanies a simultaneous improvement in the other. One strategy for exploiting these opportunities is to incorporate high plant population density trials into inbred line development programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".