Evaluation of Grain Yield Stability in Lentil Genotypes Using Non-parametric Statistics and AMMI Analysis

Articles in Press

Document Type : Original Article

Authors

• amin namdari ¹
• payam pezeshkpour ²
• mohamad barzali ³
• asghar mehraban ⁴
• amir mirzaie ⁵
• mostafa haghpanah ⁶

¹ Dryland Agricultural Research Institute, Kohgiloyeh and Boyerahmad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gachsaran

² Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorramabad, Iran

³ golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), gorgan, Iran

⁴ Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Moghan, Iran

⁵ Ilam Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ilam, Iran

⁶ Dryland Agricultural Research Institute, Kohgiloyeh and Boyerahmad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gachsaran, Iran

10.22067/ijpr.2025.90778.1101

Abstract

Extended Abstract Introduction and Objective: To identify and introduce cultivars with high yield and stability, evaluating the genotype-by-environment interaction in multi-environment trials is of paramount importance. One of the most significant challenges for lentil breeders is selecting genotypes with high yield across diverse environments. Assessing genotypes in different environments can lead to the identification of genotype(s) with high yield potential and yield stability. Genotypes exhibiting lower responses to environmental effects typically have smaller genotype × environment interactions and are desirable selections for breeders, especially if they demonstrate higher yield potential compared to checks. Various univariate and multivariate methods exist for evaluating genotype-environment interactions. This study evaluated the grain yield stability of advanced lentil genotypes in tropical climates using two methods: univariate (non-parametric statistics) and multivariate (additive main effects and multiplicative interaction, AMMI).
Materials and Methods
Fifteen selected genotypes along with two check cultivars, Gachsaran and Sephar, were evaluated in a randomized complete block design with three replications across eight environments (four warm and semi-warm regions: Gachsaran, Khorramabad, Moghan, and Ilam over two years). Each experimental plot consisted of four rows, 6 meters long, with a plant density of 200 plants per square meter. Grain yield was measured after harvest. To analyze the data, after ensuring the homogeneity of error variances across environments using Bartlett's test, a combined analysis of variance was performed for the eight studied environments. The METAN package in the Rver4.3.2 statistical software was used for AMMI analysis, calculation of non-parametric statistics, and plotting AMMI1 and AMMI2 biplots.
Findings:
The results of the combined analysis of variance for grain yield (based on the AMMI model) showed that the effects of environment, genotype and the interaction of genotype × environment were significant at the statistical probability level of 1%. Considering the significance of the genotype × environment interaction, it is possible to perform stability analyze on data in order to interpret this interaction. Stability analysis using the non-parametric TOP statistic, identified genotypes 12 and 10 as the most stable genotypes. The Kang statistic also ranked genotypes 12 and 13 as the best genotypes. Among the non-parametric statistics, TOP and Kang placed high-yielding genotypes at the top of the yield stability rankings, whereas the rankings based on Thennarasu , Nassar and Huehn statistics favored genotypes with lower mean yields. According to the Thennarasu statistic, genotypes 5, 6, and 2 were identified as the most stable, but all three had low mean yields. Similarly, genotypes with high yield stability based on the Nassar and Huehn statistics had low mean yields. By calculating the SIIG statistic and considering all non-parametric statistics, genotype 12 was found to have the best combination of yield and yield stability. In addition to this genotype, genotypes 13, 1, and 11 could also be considered desirable, considering both yield and stability.
One of the most widely used multivariate methods for stability analysis is the additive main effects and multiplicative interaction (AMMI) model. The results of the combined analysis of variance for grain yield based on the AMMI model showed that the first three principal components of the genotype × environment interaction were significant, explaining approximately 40.4%, 26.5%, and 19.8% (a total of 86.7% for the first three components and 67.1% for the first two components) of the genotype × environment interaction, respectively. The AMMI1 biplot, considering mean grain yield and the value of the first principal component, identified genotypes 10, 11, and 12 as superior genotypes. The AMMI2 biplot, considering the values of the first and second principal components, also identified genotype 12 as the most stable in terms of grain yield. The two check cultivars, Gachsaran and Sephar, showed lower yield stability than the superior genotypes based on both biplots. Genotypes located in the center of the biplot have general stability and are recommended for cultivation in most of the investigated environments. On the other hand, the genotypes that are distributed far from the center of the biplot and in the vicinity of special environments have private stability to those environments. in this regard, genotype 3  showed private adaptation to environment 4, and also genotypes 13 and 14 have private adaptation to environments 2 and 1.
Conclusion:
Based on the obtained results, it can be concluded that genotype 12 is by far the best genotype in terms of high yield and stability. Therefore, this genotype can be considered a promising candidate for release as a new cultivar in warm and semi-warm regions.

Keywords

• Adaptation
• SIIG
• Stable genotype
• Tropical and subtropical climate

Main Subjects

• Biotechnology

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References

Abdulahi, A., Mohammadi, R., & Pourdad, S. S. (2007). Evaluation of safflower (Carthamus spp.) genotypes in multi-environment trials by nonparametric methods. Asian Journal of Plant Sciences, 6(5), 827–832. https://doi.org/10.3923/ajps.2007.827.832

Aghaei-Sarbarze, M., Dastfal, M., Farzadi, H., Andarzian, B., Shahbazpour-Shahbazi A, Bahari, M., & Rostami, H. (2014). Evaluation of yield and yield stability of durum wheat genotypes in tropical and dried region of Iran. Seed and Plant Improvement Journal, 28, 315-325. (In Persian). https://doi.org/10.22092/spij.2017.111109

Amelework, A. B., Bairu, M. W., Marx, R., Laing, M., & Venter, S. L. (2023). Genotype × environment interaction and stability analysis of selected cassava cultivars in South Africa. Plants, 12(13), 2490. https://doi.org/10.3390/plants12132490

Farshadfar, E., Mahmudi, N., & Sheibanirad, A. (2014). Nonparametric methods for interpreting genotype×environment interaction in bread wheat genotypes. Journal of Biodiversity and Environmental Sciences, 4(3), 55–62.

Fox, P. N., Skovmand, B., Thompson, B. K., Braun, H. J., & Cormier, R. (1990). Yield and adaptation of hexaploid spring triticale. Euphytica, 47(1), 57–64. https://doi.org/10.1007/BF00040364

Haghpanah, M., Kazemitabar, S. K., Hashemi, S. H., & Alavi, S. M. (2015). Evaluation of Mazandaran nettle (Urtica dioica) population structure and genetic diversity by AFLP markers. Iranian Journal of Rangelands and Forests Plant Breeding and Genetic Research, 22(2), 241-250. (In Persian). https://doi.org/10.22092/ijrfpbgr.2015.12227

Jamshidmoghaddam, M., & Pourdad, S. S. (2013). Genotype × environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran. Euphytica, 190(3), 357–369. https://doi.org/10.1007/s10681-012-0776-z

Kang, M. S. (1993). Simultaneous selection for yield and stability in crop performance trials: Consequences for growers. Agronomy Journal, 85(3), 754–757. https://doi.org/10.2134/agronj1993.00021962008500030042x

Katsenios, N., Sparangis, P., Leonidakis, D., Katsaros, G., Kakabouki, I., Vlachakis, D., & Efthimiadou, A. (2021). Effect of genotype × environment interaction on yield of maize hybrids in Greece using AMMI analysis. Agronomy, 11(3), 479. https://doi.org/10.3390/agronomy11030479

Katsura, K., Tsujimoto, Y., Oda, M., Matsushima, K., Inusah, B., Dogbe, W., & Sakagami, J. I. (2016). Genotype-by-environment interaction analysis of rice (Oryza spp.) yield in a floodplain ecosystem in West Africa. European Journal of Agronomy, 73, 152–159. https://doi.org/10.1016/j.eja.2015.11.014

Najafi Mirak, T., Dastfal, M., Andarzian, B., Farzadi, H., Bahari, M., & Zali, H. (2018). Stability analysis of grain yield of durum wheat promising lines in warm and dry areas using parametric and non-parametric methods. Journal of Crop Production and Processing, 8(2), 79-96. (In Persian).

Namdari, A., Pezeshkpoor, P., Mehraban, A., Mirzaei, A., & Vaezi, B. (2022a). Evaluation of grain yield stability of advanced rainfed lentil genotypes using multivariate AMMI method. Journal of Crop Breeding, 14(42), 169-176. (In Persian). https://doi.org/10.52547/jcb.14.42.169

Namdari, A., Pezeshkpour, P., Mehraban, A., Naseri, A., Vaezi, B., & Nazarli, H. (2022b). Evaluation the grain yield stability of promising rainfed lentil genotypes using parametric and non-parametric statistics. Iranian Journal of Field Crop Science, 53(3), 145-159. (In Persian). https://doi.org/10.22059/IJFCS.2021.330502.654854

Nassar, R., & Huehn, M. (1987). Studies on estimation of phenotypic stability: Tests of significance for nonparametric measures of phenotypic stability. Biometrics, 43, 45–53. https://doi.org/10.2307/2531947

Olivoto, T., & Lúcio, A. D. (2020). metan: An R package for multi‐environment trial analysis. Methods in Ecology and Evolution, 11(6), 783–789. https://doi.org/10.1111/2041-210X.13384

Omrani, A., Omrani, S., Khodarahmi, M., Shojaei, S. H., Illés, Á., Bojtor, C., Mousavi, S. M. N., & Nagy, J. (2022). Evaluation of grain yield stability in some selected wheat genotypes using AMMI and GGE biplot methods. Agronomy, 12(5), 1130. https://doi.org/10.3390/agronomy12051130

Pezeshkpour, P., & Karimizadeh, R. (2023). Evaluation of the mean performance and stability of chickpea genotypes by integration AMMI and BLUP models and selection based on Multi-Trait Stability Index (MTSI). Journal of Crop Breeding, 15(46), 73-83. (In Persian). https://doi.org/10.61186/jcb.15.46.73

Pour-Aboughadareh, A., Khalili, M., Poczai, P., & Olivoto, T. (2022). Stability indices to deciphering the Genotype-by-Environment Interaction (GEI) effect: An applicable review for use in plant breeding programs. Plants, 11(3), 414. https://doi.org/10.3390/plants11030414

Senguttuvel, P., Sravanraju, N., Jaldhani, V., Divya, B., Beulah, P., Nagaraju, P., Manasa, Y., Prasad, A. S. H., Brajendra, P., Gireesh, C., Anantha, M. S., Suneetha, K., Sundaram, R. M., Madhav, M. S., Tuti, M. D., Subbarao, L. V., Neeraja, C. N., Bhadana, V. P., Rao, P. R., Voleti, S. R., & Subrahmanyam, D. (2021). Evaluation of genotype by environment interaction and adaptability in lowland irrigated rice hybrids for grain yield under high temperature. Scientific Reports, 11(1), 15825. https://doi.org/10.1038/s41598-021-95264-4

Singh, C., Gupta, A., Gupta, V., Kumar, P., Sendhil, R., Tyagi, B., Singh, G., Chatrath, R., & Singh, G. (2019). Genotype x environment interaction analysis of multi-environment wheat trials in India using AMMI and GGE biplot models. Crop Breeding and Applied Biotechnology, 19(3), 309–318. https://doi.org/10.1590/1984-70332019v19n3a43

Song, W., Sun, S., Ibrahim, S. E., Xu, Z., Wu, H., Hu, X., Jia, H., Cheng, Y., Yang, Z., Jiang, S., Wu, T., Sinegovskii, M., Sapey, E., Nepomuceno, A., Jiang, B., Hou, W., Sinegovskaya, V., Wu, C., Gai, J., & Han, T. (2019). Standard cultivar selection and digital quantification for precise classification of maturity groups in soybean. Crop Science, 59(5), 1997–2006. https://doi.org/10.2135/cropsci2019.02.0095

Subedi, M., Khazaei, H., Arganosa, G., Etukudo, E., & Vandenberg, A. (2021). Genetic stability and genotype × environment interaction analysis for seed protein content and protein yield of lentil. Crop Science, 61(1), 342–356. https://doi.org/10.1002/csc2.20282

Sun, X., Ma, P., & Mumm, R. H. (2012). Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding. PLoS ONE, 7(11), e50604. https://doi.org/10.1371/journal.pone.0050604

Thennarasu, K. (1995). On certain non-parametric procedures for studying genotype environment interactions and yield stability. IARI, Division of Agricultural Statistics. New Delhi.

Whitley, E., & Ball, J. (2002). Statistics review 6: Nonparametric methods. Critical Care, 6(6), 509. https://doi.org/10.1186/cc1820

Zali, H., & Barati, A. (2020). Evaluation of selection index of ideal genotype (SIIG) in other to selection of barley promising lines with high yield and desirable agronomy traits. Journal of Crop Breeding, 12(34), 93-104. (In Persian).

Zali, H., Farshadfar, E., & Sabaghpour, S. H. (2011). Non-parametric analysis of phenotypic stability in chickpea (Cicer arietinum L.) genotypes in Iran. Crop Breeding Journal, 1, 89-100. (In Persian).

Zeinalzadeh-Tabrizi, H., Mansouri, S., & Fallah-Toosi, A. (2021). Evaluation of seed yield stability of promising sesame lines using different parametric and nonparametric methods. Plant Genetic Researches, 8(1), 43-60. (In Persian). https://doi.org/10.52547/pgr.8.1.4