Evidence map›Paper›PMID 42218389›Full record

ArticleBMC plant biology2026

Integrating AMMI, Bayesian-AMMI, BLUP, and GGE biplot models to improve the identification of high-yielding and stable barley genotypes.

Alireza Pour-Aboughadareh, Hassan Zali, Ahmad Gholipour, Akbar Marzooghian, Shirali Koohkan, Ali Omrani, Kamal Shahbazi-Homonloo, Abolghasem Moradgholi, Masumeh Kheirgoo, Bita Jamshidi and 2 more

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Article in BMC plant biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Alireza Pour-AboughadarehSeed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. a.poraboghadareh@gmail.com.
Hassan ZaliCrop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Darab, Iran.
Ahmad GholipourCrop and Horticultural Science Research Department, Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gonbad, Iran.
Akbar MarzooghianCrop and Horticultural Science Research Department, Khuzestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ahvaz, Iran.
Shirali KoohkanCrop and Horticultural Science Research Department, Sistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Zabol, Iran.
Ali OmraniCrop and Horticultural Science Research Department, Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Moghan, Iran.
Kamal Shahbazi-HomonlooCrop and Horticultural Science Research Department, Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Moghan, Iran.
Abolghasem MoradgholiCrop and Horticultural Science Research Department, Sistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Zabol, Iran.
Masumeh KheirgooCrop and Horticultural Science Research Department, Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gonbad, Iran.
Bita JamshidiDepartment of Food Security and Public Health, Khabat Technical Institute, Erbil Polytechnic University, Erbil, Iraq.
Mohammad DabiriDepartment of Molecular Biology, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, 842 15, Slovak Republic.
Jan BocianowskiDepartment of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego Street 28, 60-637, Poznań, Greater Poland Voivodeship, Poland. jan.bocianowski@up.poznan.pl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe evaluation of genotype-by-environment interaction (GEI) through multi-environment trials (METs) is an essential prerequisite in breeding improvement programs targeting wide adaptation. In this study, a panel of newly developed barley genotypes was examined under field conditions at five experimental sites across Iran's warm climatic zone during the 2023-2025 growing seasons.

resultsThe pooled analysis of variance clearly demonstrated that effects of genotype (G), environment (E), and GEI influenced all measured traits, including the number of days to heading (DH), days to physiological maturity (DM), grain filling period (GFP), plant height (PH), 1000-kernel weight (TKW), and grain yield (GY). Analytical outputs obtained from the additive main effects and multiplicative interaction (AMMI) model, as well as the best linear unbiased prediction (BLUP) approach, highlighted the substantial contribution of genotype × environment interaction to variation in grain yield. Insights from genotype-trait (GT) biplot analysis indicated that TKW and GFP were positively associated with GY. When genotypes were ranked using the multi-trait stability index (MTSI), G5 (1.689), G4 (1.914), G20 (2.380), and G14 (2.508) achieved the highest scores, reflecting superior performance and stability across test environments. A comprehensive evaluation strategy integrating classical AMMI, BLUP, Bayesian AMMI, and genotype-by-environment (GGE) biplot methodologies was employed to identify genotypes that combine productivity with stability. Across all derived stability parameters, including the weighted average of absolute scores (WAAS) and its yield-weighted counterpart (WAASY), genotype G4, derived from the pedigree [Sahra/3/Bda/Rhn-03//ICB-107766], consistently outperformed other candidates. This genotype exhibited a robust yield response, high stability, and broad environmental responsiveness. Independent validation using the stability Mahalanobis distance (SM) index and Y × WAASY biplot visualization further substantiated these findings.

conclusionIn conclusion, genotype G4 represents a strong candidate for subsequent validation trials and potential varietal release in the warm agroecological regions of Iran and other areas with comparable climatic conditions.

Indexed as

Gene-Environment InteractionHordeumBayes TheoremGenotypeIranModels, GeneticPlant BreedingBayesian-AMMIBroad adaptabilityGE interactionIdeal genotypesMahalanobis distance stability index

Identifiers

PMID42218389
PMCPMC13435704

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.