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Evaluating AMMI and BLUP models for the identification of high-yielding barley genotypes adapted to cold rainfed regions in Iran | ||
Iranian Journal of Genetics and Plant Breeding | ||
دوره 13، شماره 1 - شماره پیاپی 25، تیر 2024، صفحه 33-49 اصل مقاله (987.05 K) | ||
نوع مقاله: Research paper | ||
شناسه دیجیتال (DOI): 10.30479/ijgpb.2024.20572.1372 | ||
نویسندگان | ||
Farhad Ahakpaz1؛ Ali Akbar Asadi* 2 | ||
1Maragheh Rainfed Agricultural Research Institute, Maragheh, Iran. | ||
2Crop and Horticultural Science Research Department, Zanjan Agricultural and Natural Resources Research and Education Center (AREEO), Zanjan, Iran. | ||
تاریخ دریافت: 09 تیر 1403، تاریخ بازنگری: 02 دی 1403، تاریخ پذیرش: 02 دی 1403 | ||
چکیده | ||
In this study, various statistical methodologies, including Additive Main effects and Multiplicative Interaction (AMMI) and Best Linear Unbiased Prediction (BLUP), were employed to identify high-yielding rainfed barley genotypes that are suitable for the cold and rainy regions of Iran. The experimental design comprised 25 barley cultivars and lines, along with three check cultivars, arranged in a randomized complete block design with four replications over three crop years (2017-2020). The AMMI analysis revealed that certain genotypes, specifically G15 and G21, demonstrated stability and adaptability across diverse environments, consistently yielding higher than other genotypes. Following the estimation of best linear unbiased predictions and conducting a stability analysis via the AMMI method, it was found that the highest yields were recorded in genotypes G6, G7, G15, G21, and G22, whereas the lowest yields were associated with genotypes G12, G25, G26, G27, and G28. According to the BLUP indices, genotypes G6, G15, G21, G20, G22, G17, G7, G9, and G19 were identified as superior in terms of grain stability and yield relative to the other genotypes. In the stability assessment utilizing a third-type biplot (yield versus WAASB (Weighted Average of Absolute Scores of the Best) index), it was noted that genotypes G2, G9, G10, G14, G16, G17, G19, G20, and G22 exhibited both high yield and stability. Furthermore, genotypes G4, G62, G7, G9, G10, G15, G16, G17, G19, G20, G21, and G22, which demonstrated the highest WAASBY (Weighted Average of Absolute Scores of the Best Yield) values, were classified as stable and high-yielding. Ultimately, when the first principal components in the AMMI analysis or GGE Biplot account for a lower percentage of genotype-environment interaction, it is advisable to employ methodologies that incorporate all significant principal components to effectively identify superior genotypes. | ||
کلیدواژهها | ||
Hordeum vulgare؛ Interaction effect؛ Multi-location؛ Rainfed؛ Stability | ||
عنوان مقاله [English] | ||
ارزیابی مدلهای AMMI و BLUP برای شناسایی ژنوتیپهای جو پرمحصول سازگار با مناطق سردسیر دیم ایران | ||
نویسندگان [English] | ||
فرهاد آهک پز1؛ علی اکبر اسدی2 | ||
1موسسه تحقیقات کشاورزی دیم مراغه، مراغه، ایران. | ||
2گروه تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی زنجان، AREEO، زنجان، ایران. | ||
چکیده [English] | ||
جهت شناسایی ژنوتیپهای پرمحصول جو دیم سازگار با شرایط آب و هوایی مناطق دیم سردسیر ایران از روشهای AMMIو ترکیب دو روش AMMIو BLUP و با استفاده از شاخصهای WAASBو WAASBYو دیگر شاخصهای مبتنی بر BLUP استفاده شد. به همین منظور، 25 رقم و لاین امیدبخش جو به همراه ارقام شاهد انصار، آبیدر و سرارود ۱ در قالب طرح بلوکهای کامل تصادفی با چهار تکرار در ایستگاههای تحقیقاتی دیم مناطق سرد ایران به مدت سه سال زراعی مورد بررسی قرار گرفتند. بر اساس تجزیه AMMI ژنوتیپهای G19، G11، G26، G5، G4، G14، G20، G18، G21، G15 و G9 دارای کمترین مقادیر مولفه اصلی اول بودند. از بین این ژنوتیپ ها، تنها ژنوتیپهای G15 و G21 دارای عملکرد بالاتر از میانگین عملکرد کل بودند و به همین دلیل بهعنوان ژنوتیپهای پایدار با سازگاری عمومی بالا معرفی شدند. پس از برآورد بهترین پیشبینیهای نااُریب خطی و انجام تجزیه پایداری به روش AMMIبر روی آنها مشخص گردید که بیشترین عملکرد در ژنوتیپهای G6، G15، G21، G7 و G22 و کمترین عملکرد در ژنوتیپهای G12، G25، G28، G26 و G27 مشاهده شد. بر مبنای شاخصهای مبتنی بر BLUP نیز ژنوتیپهایG6 ،G15 ،G21 ،G20 ، G22، G17، G7، G9 وG19 از نظر پایداری و عملکرد دانه نسبت به سایر ژنوتیپها برتر بودند. همچنین با استفاده از بایپلات نوع سوم (عملکرد در مقابل WAASB) مشخص شد که ژنوتیپهای G19، G20، G22، G10، G9، G16، G2، G14 و G17 دارای عملکرد بالا و پایدار بودند. شاخص پایداری ژنوتیپی، WAASBY، در ارزیابی پایداری نشان داد که ژنوتیپهای G19، G22، G20، G9، G10، G4، G7، G16، G17، G6، G15 و G21 با داشتن بیشترین مقدارWAASBY، ژنوتیپهای پایدار و دارای عملکرد بالا بودند. در نهایت با استفاده از وزنهای مختلف برای هر دو شاخص عملکرد و پایداری مشاهده شد که ژنوتیپهای G9، G4، G22، G20، G19، G16 و G10 نسبت به بقیه ژنوتیپها دارای عملکرد بیشتر و پایدارتر بودند. | ||
کلیدواژهها [English] | ||
پایداری, اثر متقابل, چند محیطی, دیم | ||
مراجع | ||
Aghaee-Sarbarzeh M., Dastfal M., Farzadi H., Andarzian B., Shahbazpour-Shahbazi A., Bahari M., and Rostami H. (2012). Evaluation of durum wheat genotypes for yield and yield stability in warm and dry areas of Iran. Seed and Plant Improvement Journal, 2: 315-325. Amini A., Asadi A. A., Chaichi M., Ezt-Ahmadi M., et al. (2023). Investigating the stability of promising bread wheat genotypes in cold climate using AMMI and GGE biplot analysis. Iranian Journal of Field Crop Science, 54(3): 119-134. Behera P. P., Sivasankarreddy K., Reddy B. J., Saharia N., Sarma R. N., Singh S. K., Majhi P. K., and Borah N. (2023). Genetic gain and selection of stable genotypes in high zinc rice using AMMI and BLUP based stability methods. Emirates Journal of Food and Agriculture, 35(12): 1-23. De Abreu H. K. A., Ceccon G., Correa A. M., Fachinelli R., Yamamoto E. L. M., and Teodoro P. E. (2019). Adaptability and stability of cowpea genotypes via REML/BLUP and GGE Biplot. Bioscience Journal, 35(4): 1071-1082. Donoso-Ñanculao G., Paredes M., Becerra V., Arrepol C., and Balzarini M. (2016). GGE biplot analysis of multi-environment yield trials of rice produced in a temperate climate. Chilean Journal of Agricultural Research, 76(2): 152-157. Dos Santos P. R., De Oliveira T. R. A., Skeen P., Nascimento M. R., da Silva Costa K. D., Araújo E. R., Pereira H. S., and Da Costa A. F. (2019). GGE Biplot and REML/BLUP based-analysis of yield stability and adaptability for common beans in multi-environment trials. Revista Brasileira de Ciências Agrarias, 14(2): 1-6. Ehyaei M., Mostafavi K., Bakhtiar F., and Mohammadi A. (2022). Yield stability of bread wheat genotypes using AMMI and GGE biplot analysis. Cereal Research, 12(2): 147-165. FAO. (2022). Statistical data, [Online] Available: http://www. FAO. org/faostat. Farshadfar E., Sabaghpour S. H., and Zali H. (2012). Comparison of parametric and non parametric stability statistics for selecting stable chickpea (Cicer arietinum L.) genotypes under diverse environments. Australian Journal of Crop Science, 6(3): 514-524. Ferreira J. R., Pereira J. F., Turchetto C., Minella E., Consoli L., and Delatorre C. A. (2016). Assessment of genetic diversity in Brazilian barley using SSR markers. Genetics and Molecular Biology, 39(1): 86-96. Gauch H. G., and Zobel R. W. (1997). Identifying mega-environments and targeting genotypes. Crop Science, 37(1): 311-326. Golkari S., Haghparast R., Roohi E., Mobasser S., et al. (2016). Multi-environment evaluation of winter bread wheat genotypes under rainfed conditions of Iran-using AMMI model. Crop Breeding Journal, 4, 5 and 6 (2; 1 and 2): 17-31, Hasani M., Hamze H., and Mansori H. (2021). Evaluation of adaptability and stability of root yield and white sugar yield (Beta vulgaris L.) in sugar beet genotypes using multivariate AMMI and GGE biplot method. Journal of Crop Breeding, 13(37): 222-235. Henderson C. R. (1984). Applications of linear models in animal breeding. Guelph, Ont.: University of Guelph. Holland J. B. (2006). Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Science, 46: 642-654. Jafari T., and Farshadfar E. (2018). Stability analysis of bread wheat genotypes (Triticum aestivum L.) by GGE biplot. Cereal Research, 8: 199-208. Hossain M. A., Sarker U., Azam M. G., Kobir M. S., et al. (2023). Integrating BLUP, AMMI, and GGE models to explore GE interactions for adaptability and stability of winter lentils (Lens culinaris Medik.). Plants, 12(11): 2079. Kanouni H., Taleei A. R., and Khalily M. (2007). Stability analysis of seed yield and one-hundred seeds weight in Desi type chickpea genotypes. Seed and Plant Journal, 23(3): 297-310. Karimizadeh R., Ghojogh H., Hosseinpour T., Armion M., Shahbazi K., and Sharifi P. (2021). Evaluating of the efficiency of AMMI and BLUP models and their integration for identifying high-yielding durum wheat (Triticum turgidum L. var. durum) genotypes adapted to warm rainfed regions of Iran. Iranian Journal of Crop Sciences, 23(1): 30-48. Karimzadeh R., Hosseinpour T., Sharifi P., Alt Jafarby J., Shahbazi Homonlo K., and Keshavarzi K. (2020). Grain yield stability of durum wheat genotypes in semi-warm rainfed regions. Cereal Research, 10(2): 135-147. Liu B. H., Knapp S., and Birkes, D. (1997). Sampling distributions, biases, variances, and confidence intervals for genetic correlations. Theoretical and Applied Genetics, 94: 8-19. Mofidian S. M. A., and Moghaddam A. (2013). Analysis of ecotype×location interaction in cold-region alfalfa ecotypes. Iranian Journal of Crop Sciences, 15(2): 181-195. Mohammadi M., Sharifi P., and Karimizadeh R. (2016). Stability analysis of seed yield of safflower genotypes (Carthamus tinctorius L.). Journal of Crop Breeding, 7(16): 104-114. Mousavi S. S., Akbarpour O. A., and Hosseinpour T. (2023). Evaluation of yield stability of bread wheat genotypes using a combination of AMMI and BLUP features. Plant Genetic Researches, 10(1): 111-122. 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: 165-18. Nardino M., Baretta D., Carvalho I. R., Olivoto T., et al. (2016). Restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) for analyzing the agronomic performance of corn. African Journal of Agricultural Research, 11: 4864-4872. Olivoto T., Lúcio A. D. C., Da Silva J. A. G., Sari B. G., and Diel M. I. (2019a). Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agronomy Journal, 111 (6): 2961-2969. Olivoto T., Lucio A. D. C., Da Silva J. A. G., Marchioro V. S., De Souza V. Q., and Jost E. (2019b). Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agronomy Journal, 111(6): 2949-2960. Olivoto T. (2019). Metan: multi environment trials analysis. R package version 1.1.0. https://github.com/TiagoOlivoto/metan (accessed 24 June 2019). Olivoto T., Nardino M., Carvalho I. R., Follmann D. N., et al. (2017). REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits. Genetics and Molecular Research, 16 (1): 1-19. Pour-Aboughadareh A. R., Marzooghian A., Gholipour A., Zali H., et al. (2024). Genotype-by-environment interaction analysis for grain yield of barley genotypes in the warm climate of Iran. Ecological Genetics and Genomics, 32: 1-9. Rahayu S. (2020). Yield stability analysis of rice mutant lines using AMMI method. IOP Conf. Series: Journal of Physics: Conference Series, 1436(1): 1-9. Resende M. D. V. (2004). Métodos estatísticos ótimos na análise de experimentos de campo. Embrapa Florestas. Embrapa Florestas, Colombo, PR. Rodriguez M., Rau D., and Papa R. (2007). Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment. Euphytica, 163(2): 231-247. Searle S. R., Casella G., and McCulloch C. (1992). Variance components. Wiley, New York. Sharifi P., Abbasian A., and Ali Mohaddesi A. (2021). Evaluation the mean performance and stability of rice genotypes by combining features of AMMI and BLUP techniques and selection based on multiple traits, Plant Genetic Researches, 7(2): 163-180. Van Eeuwijk F. A., Bustos-Korts D. V., and Malosetti M. (2016). What should students in plant breeding know about the statistical aspects of genotype×environment interactions. Crop Science, 56(5): 2119-2140. Valizadeh M., and Moghadam M. (2010). Experimental designs in agriculture. Fourth Edit, Privar Publishers, Iran. Yang R. C. (2010). Towards understanding and use of mixed-model analysis of agricultural experiments. Canadian Journal of Plant Science, 90: 605-627. Yang R. C. (2002). Likelihood-based analysis of genotype–environment interactions. Crop Science, 42: 1434-1440. Yan W., and Hunt L. A. (2001). Interpretation of genotype×environment interaction for winter wheat yield in Ontario. Crop Science, 41: 19-25. Yan W., Hunt L. A., Sheny Q., and Szlavnics Z. (2000). Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science, 40: 597-605. Zali H., and Barati A. (2020). Evaluation of selection index of ideal genotype (SIIG) in other to | ||
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