Genotype–environment interaction and yield stability analysis of Colombian yellow diploid potato cultivars using mixed linear models and environmental covariates
Bibliographic record
Abstract
Abstract Several statistical methods have been developed to decompose the phenotypic response based mainly on the general means, the effect of the genotype, and its interaction with the environment, treating the environmental variance as a confounding factor. However, the estimation of the environmental effect from the traits evaluated would establish a dependency on the genotype, resulting in a lower fit and predictive potential of the model. The integration of environmental covariates into the models that associate edaphoclimatic characteristics with the traits of interest has been proposed, with the purpose of increasing the predictive potential and the variance contained by the models. The aim of this study was to evaluate the sensitivity of the traits of interest in yellow diploid potato ( Solanum tuberosum L. Group: Phureja) crops to environmental covariates, selecting the most relevant covariates as parameters in empirical multiple regression models based on environmental variance. The results showed high yield variability due to covariates of the water component, while the quality traits were mainly affected by traits of the energetic and physicochemical components of the soil. The adjusted models explained the variance intrinsically due to the environment, reaching adjustments greater than 20%. Therefore, it is concluded that the traits have a high phenotypic sensitivity, and the incorporation of environmental covariates into the genotype‐by‐environment interaction analysis models could improve the understanding of the stability and adaptability of cultivars from the data obtained in multi‐environment trials.
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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".