Genetic Components of Yield Stability in Maize Breeding Populations
Bibliographic record
Abstract
Phenotypic stability has long been recognized as an important target in plant breeding. Stability is influenced in part by the genetic structure, i.e., level of heterogeneity and heterozygosity, of the cultivar. Yet, very little is known about the genetic components underlying stability, and how population improvement strategies influence stability. We examined 12 maize (Zea mays L.) breeding populations selected via reciprocal recurrent selection (RRS), selfed progeny recurrent selection (S), or a method combining RRS and S (COM), to examine changes in the genetic structure of the phenotypic stability of three traits (grain yield, grain moisture, and broken stalks), and two associated selection indices. Partitioning of the genotype × environment sums of squares from diallel matings of the original (C0) and advanced (CA) cycle populations into linear trends indicated that only grain yield and the unadjusted performance index (UPI) followed a predictable linear response. Grain yield and UPI linear trends were further partitioned by Gardner and Eberhart Analysis III to examine the genetic components of stability. We found that recurrent selection (RS) improved grain yield stability, and that this trait is heritable, predictable, and mostly controlled through additive gene action. Improvement in grain yield stability was observed both in cross and per se performance and was accompanied by significant improvement in the mean performance of the populations. However, the improvement in grain yield stability did not result in substantial changes in the general combining ability (gi) estimates of most populations. Our results indicate that grain yield stability can be improved through RS by selecting solely for mean performance across multiple environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".