Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds
Bibliographic record
Abstract
Abstract Plant breeders face the challenge of genotype × environment interaction (G × E) in comprehensively breeding for expanded geographic regions. An improved understanding of G × E sensitivity of traits and the environmental features that effectively discriminate among genotypes will enable more efficient breeding efforts. In this study of 31 maize ( Zea mays L.) inbreds grown in 36 environments that are part of the Genomes to Fields Initiative, we measured 14 traits, including flowering date, height, and yield components (i.e., ear and kernel dimensions) to (i) identify traits that are the most sensitive indicators of G × E; (ii) determine how geographic location and weather factors influence environments’ discriminability of inbreds; and (iii) detect patterns of stability in better and worse discriminating environments. Genotype × environment interaction explained between 9.0–20.4% of the phenotypic variance with greater effects in the yield‐component traits. Discriminability of environments varied by trait. Midwest locations (where 26 of the 31 inbreds were developed) were among the most discriminating environments for more traits, while environments in the West and East tended to be less discriminating. Weather factors during silking were significantly different between the most and least discriminating environments more often than average weather across the season or during the period from planting to silking. Stability of genotypes varied by trait, and performance was usually not correlated with stability. The dissection of complex traits, such as yield into component traits, appears to be a useful approach to understand how environmental factors differentially affect phenotype.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".