Multi-trait stability index in the selection of high-yielding and stable barley genotypes - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Appl Genet . 2025 Aug 7;67(2):317–323. doi: 10.1007/s13353-025-00998-w Search in PMC Search in PubMed View in NLM Catalog Add to search Multi-trait stability index in the selection of high-yielding and stable barley genotypes Alireza Pour-Aboughadareh Alireza Pour-Aboughadareh 1 Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, 3183964653 Iran Find articles by Alireza Pour-Aboughadareh 1 , Bita Jamshidi Bita Jamshidi 2 Department of Food Security and Public Health, Khabat Technical Institute, Erbil Polytechnic University, Erbil, 44001 Iraq Find articles by Bita Jamshidi 2 , Omid Jadidi Omid Jadidi 3 Department of Plant Breeding and Biotechnology, Science and Research Branch, Islamic Azad University, Tehran, 14778-93855 Iran Find articles by Omid Jadidi 3 , Jan Bocianowski Jan Bocianowski 4 Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, 60-637 Poznań, Poland Find articles by Jan Bocianowski 4, ✉ , Janetta Niemann Janetta Niemann 5 Department of Genetics and Plant Breeding, Poznań University of Life Sciences, 60-637 Poznań, Poland Find articles by Janetta Niemann 5 Author information Article notes Copyright and License information 1 Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, 3183964653 Iran 2 Department of Food Security and Public Health, Khabat Technical Institute, Erbil Polytechnic University, Erbil, 44001 Iraq 3 Department of Plant Breeding and Biotechnology, Science and Research Branch, Islamic Azad University, Tehran, 14778-93855 Iran 4 Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, 60-637 Poznań, Poland 5 Department of Genetics and Plant Breeding, Poznań University of Life Sciences, 60-637 Poznań, Poland Communicated by: Izabela Pawłowicz ✉ Corresponding author. Received 2025 Feb 14; Revised 2025 Jul 14; Accepted 2025 Jul 25; Issue date 2026. © The Author(s) 2025 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13079529 PMID: 40770158 Abstract The analysis of genotype-by-environment interaction (GEI) in multi-environmental trials (METs) represents a crucial component of breeding programs prior to the release of new commercial cultivars tailored for specific regions or diverse environmental conditions. Moreover, emphasizing individual traits during selection can yield misleading conclusions. Consequently, the implementation of robust selection models is essential for identifying superior genotypes based on multiple traits. The present dataset demonstrates the utility of the multi-trait stability index (MTSI) in identifying high-yielding and stable barley genotypes across ten diverse environments. The evaluated phenological and agronomic traits included days to heading, days to physiological maturity, grain-filling period, plant height, thousand-kernel weight, and grain yield. A combined analysis of variance (ANOVA) revealed significant effects attributable to environments (E), genotypes (G), and their interaction (GEI) across all assessed traits. Correlation analysis further indicated positive associations between all measured traits and grain yield. In the MTSI model, three first factors accounted for 75% of the total phenotypic variation observed across the test environments. The highest selection gain percentages were recorded for thousand-kernel weight and grain yield. Among the genotypes evaluated, G3, G10, and G14, characterized by the lowest values of the MTSI index, were identified as superior in terms of grain yield, stability, and desirable agronomic attributes. In conclusion, the findings highlight the efficacy of the MTSI in reliably identifying superior genotypes in METs. The results demonstrate that the MTSI index not only enhances the efficiency of the selection process but also improves the accuracy of genotype evaluation and ranking across heterogeneous environmental conditions. This underscores the potential of the MTSI index to support informed breeding decisions, ultimately facilitating the development of high-performing plant varieties that exhibit both yield stability and adaptability across diverse environments. Keywords: Multi-environment trial (MET), Grain yield, Selection model, Yield stability, Genotype-by-environment interaction (GEI) Introduction Genotype-by-environment interaction (GEI) describes the differential responses of genotypes to varying environmental conditions. This phenomenon can significantly affect the reliability of selecting superior genotypes, as certain genotypes may perform exceptionally well in one environment but perform poorly in another. Understanding GEI enables breeders to identify genotypes that are well adapted to specific environments, thereby improving crop performance and yield stability. Additionally, GEI analysis facilitates the identification of stable genotypes that demonstrate consistent performance across diverse environments, ensuring reliable agricultural production (Pour-Aboughadareh et al. 2019 ). Furthermore, assessing GEI contributes to risk management by identifying genotypes resilient to environmental stressors such as drought or disease. In this context, METs are integral components of breeding programs, providing critical insights into genotype performance across a range of environmental conditions. The primary objective of these trials is to identify genotypes with consistent performance under diverse environments, thus supporting the selection of superior varieties for crop improvement programs (Olivoto et al. 2019 ). Consequently, METs are indispensable for understanding and interpreting the effects of GEI (Pour-Aboughadareh et al. 2022 ). Although grain yield is the primary economic trait targeted in breeding programs, other agronomic traits associated with plant growth and development are equally important when selecting genotypes to be introduced as new varieties. Therefore, a comprehensive selection model that incorporates multiple traits is essential. The multi-trait stability index (MTSI) has recently been proposed as a robust statistical tool for evaluating the stability and performance of genotypes across multiple environments by integrating the Additive Main Effects and Multiplicative Interaction (AMMI) model and Best Linear Unbiased Prediction (BLUP) (Olivoto et al. 2019 ). This index is particularly effective for assessing GEI effects, as it simultaneously considers multiple traits, providing a holistic evaluation of genotype performance. The MTSI is calculated by combining the AMMI model, which decomposes GEI into additive and multiplicative components, with BLUP, which estimates the genetic values of genotypes. This integration allows for a more precise assessment of genotype stability across different environments (Olivoto et al. 2019 ). In a previous study (Rahmati et al. 2024 ), grain yield data were used to assess the stability of barley genotypes through the GGE biplot methodology. In the present dataset, the application of the MTSI index demonstrates its efficiency in identifying high-yielding and stable barley genotypes, with particular emphasis on additional agronomic traits. This highlights the value of MTSI as a comprehensive tool for selecting superior genotypes in breeding programs. Material and methods Descriptions of test genotypes and experimental sites The plant genetic materials investigated in the present study comprised 18 promising barley genotypes, in addition to two local cultivars [Oxin and Golchin] which served as reference genotypes (Table 1 ). These reference genotypes are characterized by high performance, stability, and broad adaptability across the warm regions of Iran. Field experiments were performed at five locations within Iran’s warm climate zone, including Ahvaz (31° 19′ 13″ N 48° 40′ 09″ E), Darab (28° 45′ 07″ N 54° 32′ 40″ E), Zabol (37° 15′ 00″ N 55° 10′ 02″ E), Moghan (39° 38′ 54″ N 47° 55′ 03″ E), and Gonbad (31° 01′ 43″ N 61° 30′ 04″ E), over two cropping seasons (2021–2022 and 2022–2023). The combinations of five locations and two cropping seasons constitute ten test environments. Among the test environments, Moghan and Gonbad are located in the northern regions, while Ahvaz, Darab, and Zabol represent the southern regions of the country. A randomized complete block design with three replications was employed at each location. Each experimental plot consisted of six rows, each 5 m in length, with a 20-cm spacing between rows. Planting and harvesting were carried out using a small-scale experimental planter and a combine harvester (Wintersteiger, Ried, Austria). Several phenological and agronomic traits, including the number of days to heading, days to physiological maturity, grain filling period (days), plant height (cm), thousand-kernel weight (g), and grain yield (Mg ha −1 ), were measured for each genotype. The phenological traits were estimated as the number of days from seed planting to heading and physiological maturity. Additional information regarding cultivation practices and other experimental details can be found in Rahmati et al. ( 2024 ). Table 1. The list of investigated barley genotypes in the present study Code Pedigree G1 Oxin [Reference] G2 Golchin [Reference] G3 Rojo/3/LB.IRAN/Una8271//Gloria"S"/Com"S"/4/Briges G4 Rojo/3/LB.IRAN/Una8271//Gloria"S"/Com"S"/4/Rihane-03 G5 Zarjau/80–5151//OK84817ICBH94-0402-0AP-0AP-17AP-0AP-12AP-11AP-0AP-0TR-0TR-0AREC G6 Zarjau/80–5151//OK84817ICBH94-0402-0AP-0AP-17AP-0AP-12AP-16AP-0AP-0TR-0TR-0AREC G7 Lignee 527/NK1272//JLB 70–63/3/Rhn-03//Lignee527/As45 G8 KAROON/KAVIR//Rhodes'S'//Tb/Chzo/3/Gloria'S'/4/Sahra/5/Yousef G9 Anoidium/Arbayan-01/3/Lignee527/NK1272//JLB70-63/4/Beecher G10 Anoidium/Arbayan-01/3/Lignee527/NK1272//JLB70-63/4/Bgs/Dujia//L.1242 G11 Bgs/Dajia//L.1242/3/(L.B.IRAN/Una8271//Gloria'S'/3/Alm/Una80//….)/4/Nosrat/5/Rhn-03//L.527/NK1272 G12 CIRU/TOCTE G13 Courlis/Rhn-03//Jonoob G14 Zahak/4/Bgs/Dajia//L.1242/3/L.B.IRAN/Una8271//Gloria'S'/3/Alm/Una80//….)/4/Rojo… G15 Rojo/3/LB.IRAN/Una8271//Gloria"S"/Com"S"/4/Gloria'S'/Copal'S'//As46/Aths/3/Rhn-03 G16 Rojo/3/LB.IRAN/Una8271//Gloria"S"/Com"S"/4/Anoidium/Arbayan-01/3/Lignee527/… G17 Merzaga(Orge077)/Alanda-01 ICB98-0908-0AP-13AP-0AP-3TR-10AP-0AP-0TR-0TR G18 PENCO/CHEVRON-BAR/6/P.STO/3/LBIRAN/UNA80//LIGNEE640/4/BLLU/5/PETUNIA 1 G19 (Salt-4)LB.Iran/Una 8271//Gloria"S"/Come"s"−11 M/3/Kavir/4/Karoon G20 W-98–10 Open in a new tab Statistical analysis The collected experimental data were analyzed using a combined ANOVA to assess the significance of environments (E), genotypes (G), and their interaction (GEI). Pearson’s correlation coefficients were calculated to examine the interrelationships among the measured traits. The likelihood ratio test (LRT) was used to evaluate whether adding a specific model term—such as the genotype effect, environment effect, or their interaction—significantly improves the fit to the data. In statistics, the likelihood-ratio test is a hypothesis test that involves comparing the goodness of fit of two competing statistical models, typically one found by maximization over the entire parameter space and another found after imposing some constraint, based on the ratio of their likelihoods. If the more constrained model (i.e., the null hypothesis) is supported by the observed data, the two likelihoods should not differ by more than sampling error. Thus, the likelihood-ratio test tests whether this ratio is significantly different from one, or equivalently, whether its natural logarithm is significantly different from zero. After then, the multi-trait stability index (MTSI) was computed to identify superior genotypes based on grain yield, stability, and other agronomic traits. MTSI combines genotype mean performance and stability across multiple environments into a single, easy-to-understand index. First, stability for each trait is measured—often using the weighted average of absolute scores biplot (WAASB) derived from singular value decomposition of genotype–environment BLUPs—so that both performance and interaction are represented on a common scale. Lower MTSI values indicate genotypes that exhibit high average performance and consistent behavior across tested environments, making them closer to an “ideotype.” More details about this index and how it is calculated can be found in Olivoto et al. ( 2019 ). All statistical analyses were performed using the ‘metan’ R package (R Core Team 2018 ), as described by Olivoto and Lucio ( 2020 ). Results and discussion Table 2 presents the results of the combined analysis of variance (ANOVA), which demonstrates significant effects for environments (E), genotypes (G), and their interaction (GEI) across all measured traits. The findings underscore the substantial genetic diversity among the evaluated genotypes, highlighting their unique characteristics and distinct responses to varying environmental conditions. This observed phenotypic variation reflects the genotypes’ differential adaptability to diverse environmental factors, underscoring the influence of genotype-by-environment interaction. Such adaptability illustrates the capacity of certain genotypes to maintain superior performance under specific environmental conditions. Furthermore, this variability emphasizes the critical importance of understanding the intricate relationship between genetic traits and environmental influences, which is essential for optimizing genotype selection and improving crop performance in breeding programs. Table 2. The results of combined analysis of variance for measured traits in barley genotypes across ten test environments Source of variation df DHE DME GFP PLH TKW YLD Environment (E) 9 32,011.11 ** 38,582.79 ** 1134.7 ** 16,193 ** 1155.67 ** 114.86 ** Replication/E 20 34.31 8.65 38.00 96.80 10.60 1.21 Genotype (G) 19 68.20 ** 20.10 ** 58.50 ** 381.60 ** 202.29 ** 1.33 ** G × E interaction 171 34.00 ** 10.50 ** 35.10 ** 107.30 ** 22.91 ** 0.72 ** Error 380 25.70 3.03 28.20 56.80 6.47 0.42 Coefficient of variation (%) 4.62 11.70 13.60 8.62 6.33 14.22 Open in a new tab ** Significant at P < 0.01. df, DHE, DME, GFP, PLH, TKW, and YLD indicate the number of degree of freedom, number of days to heading, number of days to physiological maturity, grain filling period, plant height, thousand kernel weight, and grain yield, respectively Figure 1 shows the variability of the measured traits among the barley genotypes across different test environments. For example, DHE ranged from 87.2 to 148.2 days, with a mean of 109.6 days, and genotype G5 exhibited the lowest value (Fig. 1 A). Genotype G1 was identified as the earliest maturing genotype, with DMA ranging from 147.6 to 150.4 days (Fig. 1 B). Additionally, this genotype displayed the shortest grain-filling period (Fig. 1 C). The average TKW was 40.2 g, with genotype G5 achieving the highest TKW (Fig. 1 E). YLD ranged from 1.7 to 6.4 Mg h −1 , with an average of 4.6 Mg h −1 . Among the genotypes, G14 showed the highest YLD, outperforming all other genotypes (Fig. 1 F). Fig. 1. Open in a new tab A Range of the number of days to heading (DHE), B physiological maturity (DMA), C the range of grain filling period (GFP), D plant height (PLH), E the range of thousand kernel weight, and F grain yield (YLD) for the investigated barley genotypes across 10 test environments in the warm regions of Iran Figure 2 shows Pearson’s correlation coefficients among all measured traits, based on averaged data across two cropping seasons and 10 test environments. The results indicated that the three phenological traits exhibited positive and significant correlations with one another. Furthermore, all phenological traits, as well as PLH, showed positively correlated with YLD. However, only the GFP was positively correlated with TKW. Additionally, PLH displayed a positive and significant correlation with all other traits. These findings highlight that all measured traits have a direct influence on grain yield, underscoring their critical role in determining overall crop productivity. The strong correlation observed suggests that improvements in any of these traits could contribute to enhanced grain yields, providing valuable insights for targeted breeding strategies aimed at optimizing yield potential. Fig. 2. Open in a new tab Pearson’s coefficient correlations between grain yield (YLD) and other measured traits based on the average data over two cropping seasons and 10 test environments. DHE, DMA, GFP, PLH, and TKW indicate the number of days to heading, days to physiological maturity, plant height, and thousand kernel weight, respectively The likelihood ratio test (LRT) showed a significant difference for G and GEI for all measured traits (Table 3 ). Analysis of variance revealed highly significant G and GEI effects for all six measured traits. Factor analysis identified three principal components explaining a cumulative 75% of the total variation. Factor 1 (31.70% explained variation) was strongly negatively correlated with DHE and DMA, suggesting an association with developmental timing. Factor 2 (26.40% explained variation) showed strong negative loadings for YLD and TKW. Factor 3 (16.90% explained variation) exhibited a strong positive correlation with GFP and a strong negative correlation with PLH. Broad-sense heritability estimates varied across traits, ranging from low for GFP (0.19) to very high for TKW (0.88) and high for PLH (0.71), with moderate heritability for YLD (0.45), DHE (0.50), and DMA (0.47). Predicted selection gains indicated the highest potential for increasing YLD (3.38%) and TKW (1.17%), while traits like DHE, DMA, GFP, and PLH showed relatively modest expected changes in the desired direction (decrease for DHE, DMA, PLH; increase for GFP). The analysis indicated that three principal factors accounted for 75% of the total phenotypic variation observed across the test environments. Broad-sense heritability estimates ranged from 0.19 (for YLD) to 0.88 (for TKW). Additionally, YLD and TKW showed the highest selection gain percentages compared to other evaluated traits, highlighting their potential for effective genetic improvement. Table 3. The result of likelihood ratio test (LRT), factor loading coefficients, broad-sense heritability, and selection gain for all measured traits in the MTSI model Model YLD DHE DMA GFP PLH TKW Explained variation G ** ** ** ** ** ** G × E interaction ** ** ** ** ** ** Factor 1 − 0.0003 − 0.925 − 0.865 − 0.371 − 0.375 − 0.076 31.70% Factor 2 − 0.820 0.024 − 0.117 0.319 0.142 − 0.854 26.40% Factor 3 − 0.137 − 0.062 − 0.015 0.669 − 0.776 0.070 16.90% Selection sense Increase Decrease Decrease Increase Decrease Increase Broad-sense heritability 0.45 0.50 0.47 0.19 0.71 0.88 Selection gain (%) 3.38 − 0.36 − 0.08 0.34 − 0.08 1.17 Open in a new tab Table 4 presents the ranking of the examined genotypes, as determined by the MTSI index. In this context, genotypes with the lowest MTSI values are recognized as having the potential to be both high-yielding and stable, demonstrating a combination of desirable agronomic traits. This analysis is critical for identifying varieties that not only achieve high yield but also exhibit resilience and reliability across diverse environmental conditions. By prioritizing genotypes with favorable MTSI scores, researchers can more effectively select candidates for breeding programs focused on enhancing agricultural productivity and sustainability. Based on the ranking in Table 3 , genotypes G3, G10, and G14 were identified as the most stable and high-yielding. These findings are consistent with a previous study by Rahmati et al. ( 2024 ), which also identified G3 and G10 as specifically adapted to southern regions and genotype G14 as a high-yielding genotype with excellent adaptability to the northern regions of the warm climate in Iran. This consistency further corroborates the reliability of the results presented in this dataset. The effectiveness of this methodology in identifying superior genotypes has been documented in several prior studies, such as Alam et al. ( 2024 ) in identifying the most stable potato genotypes and Benakanahalli et al. ( 2021 ) in screening guar genotypes. Table 4. The ranking pattern for barley genotypes studied based on the MTSI index Code MTSI Rank Code MTSI Rank Code MTSI Rank Code MTSI Rank G1 5.82 16 G6 5.32 11 G11 4.99 9 G16 5.81 15 G2 4.57 6 G7 5.04 10 G12 6.02 17 G17 4.39 5 G3 3.79 2 G8 6.17 18 G13 3.91 4 G18 5.41 13 G4 4.68 8 G9 5.47 14 G14 3.90 3 G19 5.37 12 G5 6.55 19 G10 3.76 1 G15 7.83 20 G20 4.64 7 Open in a new tab Conclusion This study focused on the critical importance of the genotype-by-environment interaction (GEI) analyses and utilized the multi-trait stability index (MTSI) as a new stability parameter for the identification of superior genotypes in the multi-environment trials. Indeed, the dataset analyzed in this study shows the importance of incorporating multiple traits for the identification of stable genotypes across multiple environments. Our results reveal substantial phenotypic variation among barley genotypes evaluated across different environments within Iran’s warm climate zone. This finding highlights the effectiveness of the MTSI as a powerful tool in crop breeding programs. Notably, integrating multiple traits into a unique selection model enhances the precision of identifying ideal genotypes. As a conclusion, three genotypes G3, G10, and G14 emerged as high-yielding and stable candidates. These genotypes merit further on-farm evaluation before their introduction as commercial cultivars. Acknowledgements The authors are thankful to Seed and Plant Improvement Research Institute (SPII) for providing experimental materials and data for this dataset report. Author contribution Conceptualization, A.P.-A.; methodology, A.P.-A., O.J.; software, A.P.-A., O.J., B.J.; validation, J.B. and J.N.; formal analysis, J.B.; investigation, A.P.-A.; resources, J.B. and J.N.; data curation, A.P.-A.; writing—original draft preparation, B.J.; writing—review and editing, A.P.-A., J.B. and J.N.; visualization, A.P.-A.; supervision, A.P.-A.; project administration, A.P.-A. and J.B.; funding acquisition, J.N. All authors have read and agreed to the published version of the manuscript. Data availability The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request. Declarations Competing interests The authors declare no competing interests. Footnotes Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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Heliyon 10:38131 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request. 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