Drought adaptation index (DAI) based on BLUP as a selection approach for drought-resilient switchgrass germplasm
Bibliographic record
Abstract
L.). A panel of 404 genotypes was evaluated under drought-stressed (CV) and well-watered (UC) conditions over four consecutive years (2019-2022). BLUP-estimated biomass yields were used to calculate the DAI, which enabled classification of genotypes into four adaptation groups: very well-adapted, well-adapted, adapted, and unadapted. The DAI was compared with conventional drought tolerance indices, including the Stress Susceptibility Index (SSI), Stress Tolerance Index (STI), Geometric Mean Productivity (GMP), and Yield Stability Index (YSI). Correlation analyses demonstrated strong agreement between DAI and these indices, supporting its validity and consistency. Biplot analyses using the Genotype plus Genotype-by-Environment Interaction (GGE) and Additive Main Effects and Multiplicative Interaction (AMMI) models revealed significant genotype-by-environment interactions (GEI) and identified J222.A, J463.A, and J295.A. A as high-performing genotypes, with J222.A exhibiting greater yield stability across treatments and years. Additionally, DAI isoline curves provided a graphical representation of differential genotype performance under drought and control conditions. These visualizations aided in distinguishing genotypes with stable and superior biomass yield across contrasting environments. Overall, the BLUP-based DAI is a robust and practical selection tool that improves the accuracy of identifying drought-resilient, high-yielding switchgrass genotypes. Its integration into breeding programs offers a comprehensive framework for improving biomass productivity and stress adaptation under variable climatic conditions. The application of DAI supports the development of climate-resilient cultivars and contributes to sustainable bioenergy and forage production systems.
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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 | 0.001 |
| 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 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".