Diversity and Mega‐Targets of Selection from the Characterization of a Barley Collection
Bibliographic record
Abstract
Germplasm exchange is essential for assuring genetic gain in a breeding program. Two aspects of breeding programs are relevant to making them compatible for germplasm exchange: the amount of genetic diversity within programs and the identification of breeding programs with similar breeding objectives and environments of selection (i.e., mega‐targets of selection). The objective of this study was to develop a data‐driven method to group breeding programs likely to be compatible for germplasm exchange and to use phenotypic characterization data of barley ( Hordeum vulgare L.) from breeding programs to illustrate this method. In two locations in Uruguay we evaluated 20 traits in 353 genotypes of barley from 23 private and public breeding programs distributed worldwide. We found significant amounts of genetic diversity for all traits, but differences in diversity among programs for only seven traits. We identified programs with high (Western Australia Department of Agriculture; University of Saskatchewan; and Svalöf Weibull Ab, Sweden) and low diversity (winter program of Osijek Agricultural Institute, Croatia; spring program of Osijek Agricultural Institute, Croatia; Saatzucht Josef Breun, Germany; Busch Agricultural Resources; USDA‐ARS, Aberdeen, ID; and University of Minnesota). We developed a methodology that groups programs with similar performance and response to the environments. We used the methodology to group the 23 breeding programs of barley into sets that might benefit most from germplasm exchange. The identification of compatible programs for germplasm exchange could be relevant for improving genetic gains in breeding programs.
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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 | 0.001 | 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".