Multi-Environment Genomic Prediction Models for Hybrid Maize Performance
Bibliographic record
Abstract
Hybrid maize breeding relies on accurate prediction of hybrid performance across diverse environments to enhance yield stability and adaptability. This study focused on developing and evaluating Multi-Environment Genomic Prediction (MEGP) models to improve the predictive accuracy of hybrid maize performance under variable environmental conditions. We first examined the principles of genomic prediction and the limitations of single-environment models before implementing MEGP frameworks that integrate genotype-by-environment (G×E) interactions through reaction norm and factor analytic approaches. Environmental variation was quantified using spatial and temporal covariates, while envirotyping provided additional insights into environmental effects on hybrid performance. A multi-year hybrid maize trial was conducted to assess the MEGP models, integrating genotypic, phenotypic, and environmental data. Results demonstrated that MEGP models significantly outperformed single-environment models in predictive accuracy and heritability estimates, highlighting their potential for more robust selection decisions. The study also explored the integration of high-throughput phenotyping, remote sensing, and machine learning techniques to further enhance model performance. Overall, MEGP models present a promising framework for accelerating hybrid maize breeding, improving climate resilience, and supporting global breeding networks through data-driven decision-making.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".