Analysis and Handling of G × E in a Practical Breeding Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Genotype by environment interaction (GE) is a reality in plant breeding and crop production, and has to be dealt with. There are but two viable options to deal with GE: to utilize it or to avoid it, depending on whether it is repeatable. Repeatable GE can be selected for (utilized) whereas unrepeatable GE has to be selected against (avoided). To utilize GE involves identifying repeatable GE, dividing the target region into subregions or megaenvironments (ME) based on the repeatable GE pattern, and selecting within ME. By definition, GE within ME is unrepeatable and has to be avoided. To avoid unrepeatable GE is to test in a sufficient number of environments (locations and years) representing the target ME and to select both high mean performance and high stability. My preferred analytic tool for identifying repeatable GE, ME analysis, representative test locations, and superior genotypes is GGE (genotypic main effect plus GE) biplots, which was demonstrated using oat ( Avena sativa L.) yield data from multilocation multiyear trials. Some important issues on GE study, in relation to genotype evaluation, were discussed. These included the framework of multiyear multilocation trials, the distinction between repeatable and nonrepeatable components of GE, the need to consider both genotypic main effect (G) and GE, and the relative importance of mean performance (G) vs. stability (GE) in genotype evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it