Analysis of genotype by environment interaction in barley across various locations in Iran using the AMMI model

Document Type : Research paper

Authors

1 Cereal Science Research Department, Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization, AREEO, Karaj, Iran.

2 Agricultural and Horticultural Science Research Department, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, AREEO, Mashhad, Iran.

3 Agricultural and Horticultural Science Research Department, South Khorasan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, AREEO, Birjand, Iran.

4 Horticulture Crop Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran.

5 Horticulture Crop Research Department, Varamin Agricultural and Natural Resources Resaerch and Education Center, AREEO, Varamin, Iran.

6 Horticulture Crop Research Department, Yazd Agricultural and Natural Resources Resaerch and Education Center, AREEO, Yazd, Iran.

7 Agricultural and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, AREEO, Shiraz, Iran.

Abstract

Barley (Hordeum vulgare) is an important global grain valued for its versatility and adaptability. This study utilized additive main effects and multiplicative interactions (AMMI) analysis to assess the adaptability and yield stability of twenty barley genotypes across eight locations, aiming to compare genotypes and identify suitable candidates. Significant genotypic variation was found for grain yield, highlighting the potential for targeted improvement. The AMMI analysis revealed that genotype, environment (location), and genotype-environment interaction (GEI), along with the first four interaction principal component axes, accounted for 86% of the yield variation. Environmental effects contributed 75.89% of the total sum of squares, while genotypic effects accounted for only 4.46%, and GEI effects for 19.65%. Isfahan and Karaj showed the highest GE interaction, indicated by their elevated IPCA1 scores in the AMMI1 biplot, while Birjand, Neishabor, and Varamin had the lowest scores and minimal GE interaction. The biplots identified genotypes 4 and 12 as the most stable and high-yielding, making them suitable for future genetic improvement programs. Conversely, genotypes 5, 10, 14, and 17 exhibited below-average yields and high IPCA1 scores, indicating instability but adaptation to specific locations. Crossbreeding contrasting genotypes could be beneficial for developing mapping populations for stability and yield genome studies in barley.

Keywords


Ahmadi J., Mohammadi A., and Najafi Mirak T. (2012). Targeting promising bread wheat (Triticum aestivum L.) lines for cold climate growing environments using AMMI and SREG GGE biplot analyses. Journal of Agricultural Science and Technology, 14: 645-657.
Amini F., Majidi M. M., and Mirlohi A. (2013). Genetic and genotype×environment interaction analysis for agronomical and some morphological traits in half-sib families of tall fescue. Crop Science, 53: 411-421.
Anandan A., Sabesan T., Eswaran R., Rajiv G., Muthalagan N., and Suresh R. (2009). Appraisal of environmental interaction on quality traits of rice by additive main effects and multiplicative interaction analysis. Cereal Research Communications, 37(1): 139-148.
Baik B. K., and Ullrich S. E. (2008). Barley for food: characteristics, improvement, and renewed interest. Journal of Cereal Science, 48: 233-242.
Bannayan M., Sanjani S., Alizadeh A., Lotfabadi S. S., and Mohamadian, A. (2010). Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field Crops Research, 118: 105-114.
Bantayehu M. (2010). Analysis and correlation of stability parameters in malting barley. African Journal of Crop Science, 17: 145-153.
Barati A., Arazmjo E., Tabatabaei S. A., and Taheri Mazandarani M. (2023). Selection of tolerant barley (Hordeum vulgare L.) genotypes to terminal drought stress based on grain yield, yield stability and stress tolerance indices. Iranian Journal of Crop Sciences, 25(3): 258-274.
Brar K. S., Manhas S., and Hegde D. M. (2012). GGE biplot analysis visualization of mean performance and stability for seed yield in safflower (Cartamus tinctorus) at diverse locations in India. International Journal of Agriculture Science and Research, 2: 77-90
Comstock R. E., and Moll R. H. (1963). Genotype-environment interactions. In: Hanson W. D., and Robinson H. F. (Eds.), Statistical genetics and plant breeding. Washington, DC: National Academy of Sciences, National Research Council, 164-196.
Ebdon J. S., and Gauch H. G. (2002). Additive main effect and multiplicative interaction analysis of national turfgrass performance trials. II Cultivar recommendations. Crop Science, 42: 497-506.
Gauch H. G. (1993). Prediction, parsimony and noise: A model can be more accurate than a data used to build it because it amplifies hidden patterns and discards unwanted noise. American Scientist, 81: 468- 478.
Gauch H. G. (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Science, 46: 1488-1500.
Gauch H. G., Piepho H. P., and Annicchiarico P. (2008). Statistical analysis of yield trials by AMMI and GGE. Further considerations. Crop Science, 48: 866-889.
Gauch H. G., and Zobel R. W. (1996). AMMI analysis of yield trials. In: Kang M. S., and Gauch H. G. (Eds.), Genotype-by-environment interaction. CRC Press, 85-122.
Hassani M., Heidari B., Dadkhodaie A., and Stevanato P. (2018). Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.). Euphytica, 214: 79. DOI: https://doi.org/10.1007/s10681-018-2160-0.
Hemadesh I., Ahmadi J., Fabriki-Ourang S., and Vaezi B. (2021). Appraising of barley promising lines relying on high grain yield and desirable agronomy traits in rainfed conditions using SIIG and ASIIG techniques. Iranian Journal of Genetics and Plant Breeding, 10(1): 11-30.
Johnson R., Stitch L., Olwell P., Lambert S., Horning M., and Cronn R. (2010). What are the best seed sources for ecosystem restoration on BLM and USFS lands? Native Plants Journal, 11: 117-130.
Kaya Y., Palta C., and Taner S. (2002). Additive main effects and multiplicative interactions analysis of yield performance in bread wheat genotypes across environments. Turkish Journal of Agriculture and Forestry, 26: 275-279.
Kumar A., Jnanesha A. C., Lal R. K., Chanotiya C. S., Venugopal S., and Swamy Y. V. V. S. (2023). Precision agriculture innovation focuses on sustainability using GGE biplot and AMMI analysis to evaluate GE interaction for quality essential oil yield in Eucalyptus citriodora Hook. Biochemical Systematics and Ecology, 107: 104603. DOI: https://doi.org/10.1016/j.bse.2023.104603.
Namdari A., Pezeshkpoor P., Mehraban A., Mirzaei A., and Vaezi B. (2022). Evaluation of genotype×environment interaction using WAASB and WAASBY indices in multi-environment yield trials of rainfed lentil (Lens culinaris L.) genotypes. Iranian Journal of Crop Sciences, 24(2): 165-180. (In Persian)
Oral E., Kendal E., and Dogan Y. (2018). Selection the best barley genotypes to multi and special environments by AMMI and GGE biplot models. Fresenius Environmental Bulletin, 27: 5179-5187.
Pacheco A., Vargas M., Alvarado G., Rodríguez F., Crossa J., and Burgueño J. (2015). GEA-R (Genotype x Environment Analysis with R for Windows) Version 4.1. https://hdl.handle.net/11529/10203, CIMMYT Research Data and Software Repository Network, V16.
Perkins J. M., and Jinks J. L. (1968). Environmental and genotype environmental components of variability IV. Non-linear interactions for multiple inbred lines. Heredity, 23: 525-535.
Pourdad S. S., and Mohammadi R. (2008). Use of stability parameters for comparing safflower genotypes in multi-environment trials. Asian Journal of Plant Science, 7(1): 100-104.
Purchase J. L., Hatting H., and Van Deventer C. S. (2000). Genotype×environment interaction of winter wheat (T. aestivum) in South Africa: stability analysis of yield performance. South African Journal of Plant and Soil, 17: 101-107.
Saeidnia F., Majidi M. M., Dehghani M. R., Mirlohi A., and Araghi B. (2021). Multi environmental evaluation of persistence and drought tolerance in smooth bromegrass (Bromus inermis): genetic analysis for stability in combining ability. Crop & Pasture Science, 72: 565-574.
Saeidnia F., Majidi M. M., Dehghani M. R., Saeidi G., and Mirlohi A. (2022). Drought tolerance and stability of native and foreign tall fescue genotypes: Comparison of AMMI and GGE biplot analyses. Agronomy Journal, 114(4): 2180-2185. DOI: https://doi.org/10.1002/agj2.21127.
Saeidnia F., Majidi M. M., and Mirlohi A. (2017a). Genetic analysis of stability in poly-crossed populations of orchardgrass. Crop Science, 57: 2828-2836.
Saeidnia F., Majidi M. M., and Mirlohi A. (2017b). Selection for high yield, combining ability, and stability in smooth bromegrass. Journal of Agricultural Science and Technology, 19: 1405-1416.
Saeidnia F., Taherian M., and Nazeri S. M. (2023). Graphical analysis of multi-environmental trials for wheat grain yield based on GGE-biplot analysis under diverse sowing dates. BMC Plant Biology, 23: 198. DOI: https://doi.org/10.1186/s12870-023-04197-9.
Taherian M., Nikkhah H. R., Aghnoum R., Sharifi Alhoseini M., Mahlooji M., Taheri Mazandrani M., Tabatabaei S. A., and Hassani F. (2022). Graphical analysis of grain yield stability for selection of suprior barley (Hordeum vulgare L.) promising lines in temperate regions of Iran. Iranian Journal of Crop Sciences, 24(1): 64-78. (In Persian)
Tarakanovas P., and Ruzgas V. (2006). Additive main effects and multiplicative interactions analysis of grain yield of wheat varieties in Lithuania. Agronomy Research, 4: 91-98.
Vita P. D., Mastrangeloa A. M., Matteua L., Mazzucotellib E., et al. (2010). Genetic improvement effects on yield stability in durum wheat genotypes grown in Italy. Field Crops Research, 119: 68-77.
Yan W. (2024). Two types of biplots to integrate multi‐trial and multi‐trait information for genotype selection. Crop Science, 64: 1608-1618
Yan W. (2001). GGE biplot-A windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal, 93: 1111-1118.
Yan W., and Tinker N. A. (2006). Biplot analysis of multi-environment trial data: principles and applications. Canadian Journal of Plant Science, 86: 623-645.
You S., and Izydorczyk M. (2007). Comparison of the physicochemical properties of barley starches after partial α-amylolysis and acid/alcohol hydrolysis. Carbohydrate Polymers, 69: 489-502.
Zobel R. W., Wright M. J., and Gauch H. G. (1988). Statistical analysis of a yield trial. Agronomy Journal, 80: 388-393.