Multi-index based analysis of genotype × environment interaction and selection of superior maize (Zea mays L.) hybrids
DOI:
https://doi.org/10.14719/pst.6072Keywords:
AMMI, genotype×environment interaction, hybrids, MTSI, stability, WAASBYAbstract
Genotype-environment interaction (GEI) plays a critical role in genotype adaptation, making it essential for selecting stable, widely adapted genotypes for cultivation. GEI estimation enables the identification of genotypes that perform consistently across diverse conditions. Models and stability indices derived from fixed-effect and/or mixed-effect models are frequently utilized for analyzing GEI and selecting genotypes. In this study, thirty hybrids developed through a diallele fashion, along with two checks, were grown across three environments during kharif 2023. Analysis of variance revealed significant contributions from the environment and GEI, alongside genotypic effects for eight traits studied, covering flowering, plant architecture and yield. Plot yield (t/ha) was subjected to additive main effects and multiplicative interaction effects (AMMI) analysis to study the stability and genotype interactions with the environment. The first two principal components (PCs) of AMMI analysis explained 69.1% and 30.9% of the total variation, respectively, identifying stable hybrids such as MH-TN-15 and MH-TN-30. The Genotype-genotype×environment (GGE) biplot further highlighted the adaptability and stability of all the genotypes, with the first two PCs explaining 86.11% of the G+GE variation. A multi-trait stability index (MTSI) was employed to select stable and high-performing genotypes across multiple traits. A comprehensive analysis of all the genotypes through various indices showed that hybrids MH-TN-15 and MH-TN-30 were consistently selected as stable and high-yielding genotypes across all indices, demonstrating higher yields than check hybrids and being identified for cultivation. These methods underscore the importance of combining yield and stability metrics for effective genotype selection in varied environments.
Downloads
References
Murdia L, Wadhwani R, Wadhawan N, Bajpai P, Shekhawat S. Maize utilization in India: An overview. American Journal of Food and Nutrition. 2016;4(6):169–76. https://doi.org/10.12691/ajfn-4-6-5
Erenstein O, Jaleta M, Sonder K, Mottaleb K, Prasanna BM. Global maize production, consumption and trade: Trends and R&D implications. Food Security. 2022;14(5):1295–319.
Cooper M, Gho C, Leafgren R, Tang T, Messina C. Breeding drought-tolerant maize hybrids for the US corn-belt: Discovery to product. Journal of Experimental Botany. 2014;65(21):6191–204. https://doi.org/10.1093/jxb/eru064
Xiao Y, Jiang S, Cheng Q, Wang X, Yan J, Zhang R, et al. The genetic mechanism of heterosis utilization in maize improvement. Genome Biology. 2021;22(1):148. https://doi.org/10.1186/s13059-021-02370-7
FAO. FAOSTAT statistical database [Internet]. [Rome]: FAO; c1997-. 2024. Available from: https://www.fao.org/faostat/en/#data.
Food USDo, Agriculture. International Production Assessment Division: U.S. Department of Food and Agriculture; 2024. Available from: https://ipad.fas.usda.gov/Default.aspx.
UPAg. Unified Portal for Agricultural Statistics: Department of Agriculture and Farmer's Welfare; 2024. Available from: https://upag.gov.in/.
Kivuva BM, Githiri SM, Yencho GC, Sibiya J. Genotype × environment interaction for storage root yield in sweetpotato under managed drought stress conditions. Journal of Agricultural Science. 2014;6(10). http://dx.doi.org/10.5539/jas.v6n10p41
Devi R, Sood V, Chaudhary H, Kumari A, Sharma A. Identification of promising and stable genotypes of oat (Avena sativa L.) for green fodder yield under varied climatic conditions of north-western Himalayas. Range Management and Agroforestry. 2019;40(1):67-76.
Ruswandi D, Syafii M, Wicaksana N, Maulana H, Ariyanti M, Indriani NP, et al. Evaluation of high yielding maize hybrids based on combined stability analysis, sustainability index, and GGE biplot. BioMed Research International. 2022;2022(1):3963850. https://doi.org/10.1155/2022/3963850
Crossa J. Statistical analyses of multilocation trials. Advances in agronomy. 1990; 44:55–85. https://doi.org/10.1016/S0065-2113(08)60818-4
Eberhart St, Russell W. Stability parameters for comparing varieties 1. Crop science. 1966;6(1):36-40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x
Finlay K, Wilkinson G. The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research. 1963;14(6):742-54. https://doi.org/10.1071/AR9630742
Perkins J. Environmental and genotype-environmental components of variability. VI. Diallel sets of crosses. 1970.
Gauch HG. Statistical analysis of regional yield trials: AMMI analysis of factorial designs. 1992.
Yan W, Hunt LA, Sheng Q, Szlavnics Z. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science. 2000;40(3):597-605. https://doi.org/10.2135/cropsci2000.403597x
Olivoto T, Lúcio ADC, da Silva JAG, Marchioro VS, de Souza VQ, Jost E. Mean performance and stability in multi-environment trials I: Combining features of AMMI and BLUP techniques. Agronomy Journal. 2019;111(6):2949-60. https://doi.org/10.2134/agronj2019.03.0220
Khan MMH, Rafii MY, Ramlee SI, Jusoh M, Al Mamun M. AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under the multi-environmental trials (METs). Scientific Reports. 2021;11(1):22791. https://doi.org/10.1038/s41598-021-01411-2
Etana D, Merga D. Additive main effect and multiplicative interaction model (AMMI) in plant breeding stability analysis: Review. Journal of Agricultural Research Pesticides and Biofertilizers. 2021;2(3). http://doi.org/07.2021/1.1037
Shojaei SH, Mostafavi K, Omrani A, Omrani S, Nasir Mousavi SM, Illés Á, et al. Yield stability analysis of maize (Zea mays L.) hybrids using parametric and AMMI methods. Scientifica. 2021;2021(1):5576691. https://doi.org/10.1155/2021/5576691
Olivoto T, Lúcio ADC, da Silva JAG, Sari BG, Diel MI. Mean performance and stability in multi-environment trials II: Selection based on multiple traits. Agronomy Journal. 2019;111(6):2961-69. https://doi.org/10.2134/agronj2019.03.0221
Piepho H, Möhring J, Melchinger A, Büchse A. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica. 2008;161(1):209-28. https://doi.org/10.1007/s10681-007-9449-8
Farshadfar E, Mohammadi R, Aghaee M, Vaisi Z. GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. Australian Journal of Crop Science. 2012;6(6):1074-79.
Purchase JL. Parametric analysis to describe genotype × environment interaction and yield stability in winter wheat: University of the Free State; 1997.
Mahmodi N, Yaghotipoor A, Farshadfar E. AMMI stability value and simultaneous estimation of yield and yield stability in bread wheat (’Triticum aestivum’ L.). Australian Journal of Crop Science. 2011;5(13):1837-44. https://doi/10.3316/informit.005709931410019
Crossa J, Cornelius PL, Yan W. Biplots of linear-bilinear models for studying crossover genotype×environment interaction. Crop Science. 2002;42(2):619-33. https://doi.org/10.2135/cropsci2002.6190
Cornelius P, Crossa J, Seyedsadr M. Statistical tests and estimators of multiplicative models for genotype-by-environment interaction. Genotype-by-environment interaction CRC Press, Boca Raton, FL. 1996:199-234.
Yan W, Tinker NA. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science. 2006;86(3):623-45. https://doi.org/10.4141/P05-169
Olivoto T, Lúcio ADC. metan: An R package for multi-environment trial analysis. Methods in Ecology and Evolution. 2020;11(6):783-9. https://doi.org/10.1111/2041-210X.13384
R Core Team. R: A language and environment for statistical computing. 2023.
BoŽOvi? D, Popovi? V, Raji?I? V, Kosti? M, Filipovi? V, Kolari? L, et al. Stability of the expression of the maize productivity parameters by AMMI models and GGE-biplot analysis. Notulae Botanicae Horti Agrobotanici Cluj-Napoca. 2020;48(3):1387-97. https://doi.org/10.15835/nbha48312058
Al-Ashkar I, Sallam M, Ibrahim A, Ghazy A, Al-Suhaibani N, Ben Romdhane W, et al. Identification of wheat ideotype under multiple abiotic stresses and complex environmental interplays by multivariate analysis techniques. Plants (Basel). 2023;12(20). https://doi.org/10.3390/plants12203540
Oyekunle M, Menkir A, Mani H, Olaoye G, Usman I, Ado S, et al. Stability analysis of maize cultivars adapted to tropical environments using AMMI analysis. Cereal Research Communications. 2017;45(2):336-45. https://doi.org/10.1556/0806.44.2016.054
Hudson AI, Odell SG, Dubreuil P, Tixier M-H, Praud S, Runcie DE, et al. Analysis of genotype-by-environment interactions in a maize mapping population. G3 Genes|Genomes|Genetics. 2022;12(3). https://doi.org/10.1093/g3journal/jkac013
Rodrigues FC, Silva FCS, Carneiro PCS, Peternelli LA, Bhering LL, da Silva FL. Environmental stratification in trials of unbalanced multiyear soybean (Glycine max (l.) Merril) via the integration of GGE biplot graphs and networks of environmental similarity. Euphytica. 2022;218(6):71. https://doi.org/10.1007/s10681-022-02994-1
Esan VI, Oke GO, Ogunbode TO, Obisesan IA. AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions. Front Plant Sci. 2022;13:997429. https://doi.org/10.3389/fpls.2022.997429
Ljubi?i? N, Popovi? V, Kosti? M, Paji? M, Bu?en M, Gligorevi? K, et al. Multivariate interaction analysis of Zea mays L. genotypes growth productivity in different environmental conditions. Plants. 2023;12(11):2165. https://doi.org/10.3390/plants12112165
Gauch Jr H, Zobel RW. Optimal replication in selection experiments. Crop Science. 1996;36(4):838–43. https://doi.org/10.2135/cropsci1996.0011183X003600040002x
Mohammadi R, Roostaei M, Ansari Y, Aghaee M, Amri A. Relationships of phenotypic stability measures for genotypes of three cereal crops. Canadian Journal of Plant Science. 2010;90(6):819-30. https://doi.org/10.4141/cjps09102
Omrani A, Omrani S, Khodarahmi M, Shojaei SH, Illés Á, Bojtor C, et al. Evaluation of grain yield stability in some selected wheat genotypes using AMMI and GGE biplot methods. Agronomy. 2022;12(5). https://doi.org/10.3390/agronomy12051130
Yang R-C, Crossa J, Cornelius PL, Burgueño J. Biplot analysis of genotype × environment interaction: Proceed with caution. Crop Science. 2009;49(5):1564-76. https://doi.org/10.2135/cropsci2008.11.0665
Reddy PS, Satyavathi CT, Khandelwal V, Patil H, Narasimhulu R, Bhadarge H, et al. GGE biplot analysis for identification of ideal cultivars and testing locations of pearl millet (Pennisetum glaucum LR Br.) for peninsular India. Indian Journal of Genetics and Plant Breeding. 2022;82(02):167-76. https://doi.org/10.31742/IJGPB.82.2.5
Hailemariam Habtegebriel M. Adaptability and stability for soybean yield by AMMI and GGE models in Ethiopia. Frontiers in Plant Science. 2022;13:950992. https://doi.org/10.3389/fpls.2022.950992
Singamsetti A, Shahi JP, Zaidi PH, Seetharam K, Vinayan MT, Kumar M, et al. Genotype × environment interaction and selection of maize (Zea mays L.) hybrids across moisture regimes. Field Crops Research. 2021;270. https://doi.org/10.1016/j.fcr.2021.108224
Patel R, Parmar DJ, Kumar S, Patel DA, Memon J, Patel MB, et al. Dissection of genotype× environment interaction for green cob yield using AMMI and GGE biplot with MTSI for selection of elite genotype of sweet corn (Zea mays conva. Saccharata var. rugosa). Indian Journal of Genetics and Plant Breeding. 2023;83(01):59-68. https://doi.org/10.31742/ISGPB.83.1.8
Koundinya A, Ajeesh B, Hegde V, Sheela M, Mohan C, Asha K. Genetic parameters, stability and selection of cassava genotypes between rainy and water stress conditions using AMMI, WAAS, BLUP and MTSI. Scientia Horticulturae. 2021;281:109949. https://doi.org/10.1016/j.scienta.2021.109949
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Himakara Datta Mandalapu, Sivakumar Subbarayan, Kumari Vinodhana N, Sathya Sheela K R V, Senthil Natesan; Uma D; Senthil A
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright and Licence details of published articles
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Open Access Policy
Plant Science Today is an open access journal. There is no registration required to read any article. All published articles are distributed under the terms of the Creative Commons Attribution License (CC Attribution 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/). Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
