A dataset for the application of the Bayesian AMMI (B-AMMI) model to identify high-yielding and stable barley genotypes.
5 affiliations
- 1Department of Plant Breeding and Biotechnology, Science and Research Branch, Islamic Azad University, Tehran, 14778-93855, Iran.
- 2Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, P.O. Box 31587-77871, Iran.
- 3Department of Plant Breeding and Biotechnology, Ker. C., Islamic Azad University, Kermanshah, Iran.
- 4Department of Food Security and Public Health, Khabat Technical Institute, Erbil Polytechnic University, Erbil, 44001, Iraq.
- 5Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Poznań, 60-637, Poland.
Abstract
One of the most crucial challenges for finding superior crop genotypes with wide or specific adaptation throughout multi-environment trials (MET) is analyzing genotype-by-environment interaction (GEI) and phenotypic stability. In this study, an existing MET dataset--including 18 advanced barley genotypes along with two check cultivars evaluated across 10 environments (five locations in Iran over two consecutive cropping seasons)--previously analyzed with classical Additive Main Effects and Multiplicative Interaction (AMMI) and GGE biplot models was comprehensively reanalyzed using a Bayesian framework. Unlike classical frequentist approaches that rely on fixed point estimates without quantifying estimation uncertainty, the Bayesian paradigm incorporates prior distributions and posterior probability density intervals, effectively mitigating overfitting and providing robust probabilistic credibility for GEI parameters. The evaluation integrated Bayesian AMMI (B-AMMI), Bayesian Weighted Average of Absolute Scores (BWAAS), the combined performance-stability index (BWAASY), and Mahalanobis stability distance. According to the B-AMMI2 biplot, the first two interaction principal component axes (IPCA1 = 41.87% and IPCA2 = 24.35%) jointly accounted for 66.22% of the GEI variance. This revealed significant environmental discrimination (E1, E3, E4, E5, E9, and E10) and delineated a mega-environment (E7, E8, and E9). While genotypes G7, G9, G12, G15, and G16 revealed specific adaptations, genotypes G8, G11, G17, G18, and G19 displayed high stability near the origin. Posterior estimations of BWAAS confirmed that G2, G10, G11, G18, and G19 were the most stable. Beyond confirming previously recognized elite lines (G3, G10, and G14), the Bayesian framework offered superior discriminative resolution: simultaneous selection via BWAASY and Mahalanobis stability distance effectively resolved ambiguity in candidate selection, revealing previously obscured top-performing stable genotypes (e.g., G3, G10, and G14) and confirming G3, G4, and G10 as possessing optimal compromises between high yield and low GEI sensitivity, while precisely classifying G14 as specifically adapted rather than broadly stable. Overall, re-evaluating MET data under the Bayesian AMMI framework provided a powerful and statistically sound probabilistic basis for breeding programs to increase selection accuracy.
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BibTeX
@article{rahmati2026dataset,
title = {{A dataset for the application of the Bayesian AMMI (B-AMMI) model to identify high-yielding and stable barley genotypes.}},
author = {Salim Rahmati and Alireza Pour-Aboughadareh and Reza Azizi-Nezhad and Lia Shooshtari and Alireza Etminan and Bita Jamshidi and Omid Jadidi and Jan Bocianowski},
journal = {Data in brief},
year = {2026},
month = aug,
doi = {10.1016/j.dib.2026.113309},
url = {https://europepmc.org/article/MED/42927261},
note = {CC BY},
}
RIS
TY - JOUR
TI - A dataset for the application of the Bayesian AMMI (B-AMMI) model to identify high-yielding and stable barley genotypes.
AU - Salim Rahmati
AU - Alireza Pour-Aboughadareh
AU - Reza Azizi-Nezhad
AU - Lia Shooshtari
AU - Alireza Etminan
AU - Bita Jamshidi
AU - Omid Jadidi
AU - Jan Bocianowski
PY - 2026
DA - 2026/08/01
JO - Data in brief
DO - 10.1016/j.dib.2026.113309
UR - https://europepmc.org/article/MED/42927261
AB - One of the most crucial challenges for finding superior crop genotypes with wide or specific adaptation throughout multi-environment trials (MET) is analyzing genotype-by-environment interaction (GEI) and phenotypic stability. In this study, an existing MET dataset--including 18 advanced barley genotypes along with two check cultivars evaluated across 10 environments (five locations in Iran over two consecutive cropping seasons)--previously analyzed with classical Additive Main Effects and Multiplicative Interaction (AMMI) and GGE biplot models was comprehensively reanalyzed using a Bayesian framework. Unlike classical frequentist approaches that rely on fixed point estimates without quantifying estimation uncertainty, the Bayesian paradigm incorporates prior distributions and posterior probability density intervals, effectively mitigating overfitting and providing robust probabilistic credibility for GEI parameters. The evaluation integrated Bayesian AMMI (B-AMMI), Bayesian Weighted Average of Absolute Scores (BWAAS), the combined performance-stability index (BWAASY), and Mahalanobis stability distance. According to the B-AMMI2 biplot, the first two interaction principal component axes (IPCA1 = 41.87% and IPCA2 = 24.35%) jointly accounted for 66.22% of the GEI variance. This revealed significant environmental discrimination (E1, E3, E4, E5, E9, and E10) and delineated a mega-environment (E7, E8, and E9). While genotypes G7, G9, G12, G15, and G16 revealed specific adaptations, genotypes G8, G11, G17, G18, and G19 displayed high stability near the origin. Posterior estimations of BWAAS confirmed that G2, G10, G11, G18, and G19 were the most stable. Beyond confirming previously recognized elite lines (G3, G10, and G14), the Bayesian framework offered superior discriminative resolution: simultaneous selection via BWAASY and Mahalanobis stability distance effectively resolved ambiguity in candidate selection, revealing previously obscured top-performing stable genotypes (e.g., G3, G10, and G14) and confirming G3, G4, and G10 as possessing optimal compromises between high yield and low GEI sensitivity, while precisely classifying G14 as specifically adapted rather than broadly stable. Overall, re-evaluating MET data under the Bayesian AMMI framework provided a powerful and statistically sound probabilistic basis for breeding programs to increase selection accuracy.
SN - 2352-3409
ER -