Kernel Number Determination in Argentinean Maize Hybrids Released between 1965 and 1993
Bibliographic record
Abstract
Grain yield and the stability of harvest index are greater in newer than in older Argentinean maize (Zea mays L.) hybrids. The objective of this study was to elucidate mechanisms underlying the superior yield and harvest index stability of newer Argentinean maize hybrids using the relationship between kernel number per plant (KNP) and plant growth rate during the period bracketing silking (PGRs). Three experiments were performed at Balcarce, Argentina, during two growing seasons (1998–2000). Maize was grown under a wide range of plant densities (from 2 up to 30 plants m−2) to generate contrasting availability of resources per plant. Growth of individual plants during the period bracketing silking was estimated through a nondestructive method on the basis of relationships between actual shoot dry matter and morphometric variables, including stem and ear diameters and ear length. Detasseling and silk pollination synchronization treatments were imposed in one experiment to also modify available resources per kernel and kernel sink strength. Newer hybrids set more kernels per unit PGRs than older hybrids as is indicated by (i) the lower threshold PGRs for kernel set and (ii) greater potential kernel number at high availability of resources per plant, for newer than for older hybrids. At low and intermediate PGRs, the greater kernel set per unit PGRs in newer vs. older hybrids was attributable to greater partitioning of dry matter to the topmost ear during the period bracketing silking, whereas number of kernels set per unit of ear growth rate did not differ. In contrast, kernel set per unit of ear growth rate was greater in newer than in older hybrids when PGRs was high. Results of this study indicate that genetic yield improvement in maize is attributable, in part, to increased partitioning of dry matter to the ear during the critical period bracketing silking.
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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) | 0.001 | 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".