Physiological Basis of Successful Breeding Strategies for Maize Grain Yield
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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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it