Kernel number and kernel weight stability can vary across corn hybrids
Bibliographic record
Abstract
Abstract The stability of a trait refers to the extent to which its expression in a given genotype varies across environments. The more stable a trait, the less variable its expression. Grain yield stability is a central consideration in corn production to ensure that hybrids perform consistently across environments and is frequently quantified given its importance. Little attention has been paid to the stability of corn yield components, kernel number per m2 (KN), and kernel weight (KW). Our hypothesis is that while previous research suggests that yield stability of commercial corn hybrids is generally consistent, the stabilities of KN and KW may exhibit significant differences, even when overall yield stability remains constant. This study evaluated the yield and yield component stabilities of 23 commercial corn hybrids conducted on‐farm at five location‐years in Ontario, Canada, using Finlay–Wilkinson regression. Most (61%) hybrids exhibited average yield stability with β1‐values close to 1.0. But seven hybrids displaying average yield stability had KN and/or KW stabilities significantly different than average. While in absolute terms, KW was always more stable than KN across environments, the data indicate that hybrids have different mechanisms to achieve stable yields in terms of relative yield component adjustments. Overall, 14 hybrids had yield component β1‐values significantly more or less stable than average. The instances where yield component β1‐values differed significantly from 1.0 were almost equally divided between KN and KW. These findings support the potential for hybrid‐specific corn management, that is, tailoring management practices to take advantage of hybrid variation in yield component stabilities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".