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Evaluation of grain yield stability of barley genotypes using additive main effects and multiplicative interaction model | ||
| Journal of Plant Physiology and Breeding | ||
| مقاله 4، دوره 14، شماره 1، 2024، صفحه 51-66 اصل مقاله (703.67 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/jppb.2024.60648.1330 | ||
| نویسندگان | ||
| Elnaz Ramzi1؛ Ali Asghari* 1؛ Asghar Ebadi* 1؛ Omid Sofalian1؛ Asghar Mehraban2 | ||
| 1Department of Agronomy and Plant Breeding, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| 2Moghan Agricultural Research Center, Moghan, Iran. | ||
| چکیده | ||
| Objective: Barley as one of the most important cereals is crucial to supplying the required energy for both humans and animals. Grain yield is strongly influenced by environmental conditions and breeders often determine the stability of high-yielding genotypes across various locations before recommending a cultivar for release. Methods: For this research, 18 barley genotypes were tested at four different research stations including Gachsaran, Moghan, Khorramabad, and Gonbad in Iran. The additive main effects and multiplicative interaction (AMMI) model was used to identify the stable genotypes. Results: Partitioning of the GE interaction indicated that the first interaction principal component axes (IPCA) captured 35.9% of the interaction sum of squares. However, the five IPCAs accounted for 87.4% of the total interaction. Moreover, 13 AMMI-based stability statistics were calculated. Using cluster analysis, AMMI stability indices were divided into four groups. This analysis showed that most indices do not correlate with the grain yield. AMMI indices were placed in separate groups with large distances from the grain yield. However, the yield stability index (YSI) showed the highest correlation with grain yield, and based on that, G13, G18, and G11 genotypes were recognized as superior genotypes. G13 and G18 genotypes had an acceptable performance in two years and four locations, so they may be regarded as genotypes with both high yield and stability in these environments. However, by using the AMMI2 mega-environmental analysis to select suitable cultivars for each mega-environment, the target region was divided into three sub-regions, and the winner genotypes of each sub-region were identified. The first mega-environment consisted of the areas Gonbad and Ghachsaran, where genotype G14 was the winner; the second mega-environment consisted of the Khorramabad area, where genotype G2 was the winner, and the third mega-environment included only the Moghan area, where genotype G13 was the winner. Conclusion: In conclusion, the AMMI2 mega-environmental analysis identified three mega environments and the best genotypes specifically adapted to these environments. Since two test areas Gonbad and Ghachsaran were located in the first mega-environment, it seems that the performance ranking of the test genotypes in these areas will be almost the same, and in coming years, the test can be performed in only one of these areas. | ||
| کلیدواژهها | ||
| AMMI؛ Barley؛ Mega-environment. SIIG index | ||
| مراجع | ||
|
Ajay BC, Aravind J, Abdul Fiyaz R, Bera SK, Kumar N, Gangadhar K, Kona P. 2018. Modified AMMI stability index (MASI) for stability analysis. ICAR-DGR Newsl. 18: 4-5.
Ajay BC, Aravind J, Fiyaz RA, Kumar N, Lal C, Gangadhar K, Kona P, Dagla MC, Bera SK. 2019. Rectification of modified AMMI stability value (MASV). Indian J Genet. 79(4): 726-731. https://doi.org/10.31742/IJGPB.79.4.11
Annicchiarico P. 1997. Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy. Euphytica. 94(1): 53-62. https://doi.org/10.1023/A:1002954824178
Becker HC. 1981. Correlations among some statistical measures of phenotypic stability. Euphytica. 30: 835-840. https://doi.org/10.1007/BF00038812
Becker HC, Léon J. 1988. Stability analysis in plant Breeding. Plant Breed. 101: 1-23. https://doi.org/10.1111/j.1439-0523.1988.tb00261.x
Cheshkova AF, Stepochkin PI, Aleynikov AF, Grebennikova IG, Ponomarenko VI. 2020. A comparison of statistical methods for assessing winter wheat grain yield stability. Vavilovskii Zhurnal Genet Selektsii. 24(3): 267-275. https://doi.org/10.18699/VJ20.619
El-Hashash EF, El-Absy KM. 2019. Barley (Hordeum vulgare L.) breeding. In: Al-Khayri J Jain S, Johnson D. (eds.) Advances in plant breeding strategies: Cereals. Cham.: Springer. https://doi.org/10.1007/978-3-030-23108-8_1
FAO. 2022. FAOSTAT. Agriculture Organization of the United Nations Statistics Division. Economic and Social Development Department, Rome, Italy.
Farshadfar E. 2008. Incorporation of AMMI stability value and grain yield in a single non-parametric index (GSI) in bread wheat. Pak J Biol Sci. 11: 1791-1796. https://doi:10.3923/pjbs.2008.1791.1796
Finlay KW, Wilkinson GN. 1963. The analysis of adaptation in plant-breeding programme. Aust J Agric Res. 14: 742-754.
Fox PN, Skovmand B, Thompson BK, Braun H-J, Cormier R. 1990. Yield and adaptation of hexaploid spring triticale. Euphytica. 47: 57-64. https://doi.org/10.1007/BF00040364
Gauch HG. 1988. Model selection and Validation for yield trials with interaction. Biometrics. 44: 705-715. http://dx.doi.org/10.2307/2531585
Gauch HG. 2007. MATMODEL version 3.0: Open source software for AMMI and related analyses. Available at http://www.css.cornell.edu/staff/gauch. Crop and Soil Sciences, Cornell Univ., Ithaca, NY.
Gauch HG, Zobel RW. 1988. Predictive and postdictive success of statistical analysis of yield trials. Theor Appl Genet. 76: 1-10. https://doi.org/10.1007/BF00288824
Gauch HG, Zobel RW. 1989. Accuracy and selection success in yield trial analyses. Theor Appl Genet. 77: 473-481. https://doi.org/10.1007/BF00274266
Gauch HG, Zobel RW. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311-326. https://doi.org/10.2135/cropsci1997.0011183X003700020002x
Gollob HF. 1968. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika. 33: 73-115. https://doi.org/10.1007/BF02289676
Jambhulkar NN, Rath NC, Bose LK, Subudhi HN, Mondal B, Das L, Meher J. 2017. Stability analysis for grain yield in rice in demonstrations conducted during rabi season in India. Oryza. 54(2): 236-240. https://epubs.icar.org.in/index.php/OIJR/article/view/75180
Kandus M, Almorza D, Boggio Ronceros R, Salerno JC. 2010. Statistical models for evaluating the genotype-environment interaction in maize (Zea mays L.). FYTON. 79: 39-46.
Kang MS, Pham HN. 1991. Simultaneous selection for high yielding and stable crop genotypes. Agron J. 83: 161-165. https://doi.org/10.2134/agronj1991.00021962008300010037x
Kanouni H, Farayedi Y, Shobeyri, SS. 2023. Yield response of Kabuli-type chickpea genotypes to different environmental conditions using AMMI and GGE biplot methods. J Plant Physiol Breed. 13(2): 29-42. https://doi.org/10.22034/JPPB.2023.17356
Lin CS, Binns MR. 1988. A superiority measure of cultivar performance for cultivar × location data. Can J Plant Sci. 68(1): 193-198. https://doi.org/10.4141/cjps88-018
Lin CS, Binns MR, Lefkovitch LP. 1986. Stability analysis: Where do we stand? Crop Sci. 26: 894-900. https://doi.org/10.2135/cropsci1986.0011183X002600050012x
Mortazavian SMM, Azizi-nia S. 2014. Nonparametric stability analysis in multi-environment trial of canola. Turk J Field Crops. 19(1): 108-117. https://doi.org/10.17557/tjfc.41390
Pacheco RM, Duarte JB, Vencovsky R, Pinheiro JB, Oliveira AB. 2005. Use of supplementary genotypes in AMMI analysis. Theor Appl Genet. 110: 812-818. https://doi.org/10.1007/s00122-004-1822-6 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 (Basel). 11(3): 414. https://doi.org/10.3390/plants11030414
Purchase JL. 1997. Parametric analysis to describe GE interaction and yield stability in winter wheat. Ph.D. Thesis, Department of Agronomy, Faculty of Agriculture, University of the Orange Free State, Bloemfontein, South Africa.
Rao AR, Prabhakaran VT. 2005. Use of AMMI in simultaneous selection of genotypes for yield and stability. J Indian Soc Agric Stat. 59: 76-82.
Romagosa M, Fox PN, García del Moral LF, Ramos JM, García del Moral B, Roca de Togores F, Molina-Cano JL. 1993. Integration of statistical and physiological analyses of adaptation of near-isogenic barley lines. Theor Appl Genet. 86: 822-826. https://doi.org/10.1007/BF00212607
Sabaghnia N, Karimizadeh R, Mohammadi M. 2012. Model selection in additive main effect and multiplicative interaction model in durum wheat. Genetika. 44(2): 325-339. https://doi.org/10.2298/GENSR1202325S
Shukla GK. 1972. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity. 29: 237-245. https://doi.org/10.1038/hdy.1972.87
Sneller CH, Kilgore-Norquest L, Dombek D. 1997. Repeatability of yield stability statistics in soybean. Crop Sci. 37(2): 383-390. https://doi.org/10.2135/cropsci1997.0011183X003700020013x
Yan W, Hunt LA. 1998. Genotype by environment interaction and crop yield. Plant Breed Rev. 16: 135-178. https://doi.org/10.1002/9780470650110.ch4
Yan W, Kang MS. 2003. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. Boca Raton, FL: CRC Press.
Yan W, Tinker NA. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Can J Plant Sci. 86(3): 623-645. https://doi.org/10.4141/P05-169
Zali H, Farshadfar E, Sabaghpour SH, Karimizadeh R. 2012. Evaluation of genotype × environment interaction in chickpea using measures of stability from AMMI model. Ann Bio Res. 3(7): 3126-3136.
Zali H, Sofalian O, Hasanloo T, Asghari A, Hoseini SM. 2015. Appraising of drought tolerance relying on stability analysis indices in canola genotypes simultaneously, using selection index of ideal genotype (SIIG) technique: Introduction of new method. Biol Forum. 7(2): 703-711.
Zobel RW. 1994. Stress resistance and root systems. In: Proceedings of the Workshop on Adaptation of Plants to Soil Stress. 1-4 August, 1993. INTSORMIL Publication 94-2, 80–99. Institute of Agriculture and Natural Resources, University of Nebraska, Lincoln, NE, USA. | ||
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