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Selecting superior groundnut (Arachis hypogaea L.) genotypes using multi-trait selection indices | ||
| Journal of Plant Physiology and Breeding | ||
| مقاله 3، دوره 14، شماره 1، شهریور 2024، صفحه 33-50 اصل مقاله (944.73 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/jppb.2024.60718.1331 | ||
| نویسندگان | ||
| Farooq Fadakar Navrood1؛ Rasool Asghari Zakaria* 2؛ Marefat Mostafavi Rad3؛ Nasser Zare4؛ Mina Moghaddaszadeh Ahrabi5 | ||
| 1Department of Plant Production and Genetics, Faculty of Agriculture and Natural Resources, University of Mohaghgh Ardabili, Ardabil, Iran | ||
| 2Department of Agronomy and Plant Breeding, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| 3Department of Horticulture and Agronomy, Agricultural and Natural Resources Research and Education Center of Guilan Province, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran | ||
| 4Department of Plant Production and Genetics, Faculty of Agriculture and Natural Resources, University of Mohaghgh Ardabili, Ardabil, Iran. | ||
| 5Department of Cellular and Molecular Biology, Faculty of Biology, Mizan University of Tabriz, Tabriz, Iran | ||
| چکیده | ||
| Objective: Groundnut (Arachis hypogaea L.) is an annual oilseed and proteinous crop whose production is mainly affected by genotype × environment interactions, making it hard to select superior genotypes. The multi-trait selection indices have been used to choose genotypes based on multiple traits. Methods: In this study, 11 groundnut genotypes were evaluated based on a randomized complete block design with three replications in three locations, Talesh, Masal, and Rasht, Guilan province, Iran, during two growing seasons (2019-2020 and 2020-2021). Variance components were estimated using the restricted maximum likelihood method, and factor analysis was applied to grouping the traits. Multi-trait genotype-ideotype distance index (MGIDI) and ideal genotype selection index (IGSI) were calculated to select superior genotypes using 16 agronomical characteristics. Results: Although by considering the 30% selection intensity, the genotypes selected by the MGIDI and IGSI indices in the three locations were somewhat different, the ICG192 groundnut genotype was selected as a superior genotype in all three areas based on both MGIDI and IGSI indices. Conclusion: The results revealed relative compliance between the MGIDI and IGSI indices in the selection of superior genotypes, and they may be used for genotype selection based on multiple traits. | ||
| کلیدواژه‌ها | ||
| Genotype-ideotype distance index (MGIDI)؛ Ideal genotype selection index (IGSI)؛ Multi-environment؛ Restricted maximum likelihood (REML) | ||
| مراجع | ||
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Aalami A, Abdullahi Mandolakani B, Esfahani M, Mozaffari J. 2007. Assessment of genetic diversity in groundnut (Arachis hypogea L.) germplasm using morphological traits. Iran J. Crop Sci. 8(4): 357-367 (In Persian with English abstract).
Bhering LL, Laviola BG, Salgado CC, Sanchez CFB, Rosado TB, Alves AA, 2012. Genetic gains in physic nut using selection indexes. Pesq Agropec Bras. 47(3): 402-408. https://doi.org/10.1590/S0100-204X2012000300012
Bizari EH, Val BHP, Pereira EDM, Mauro AOD, Uneda-Trevisoli SH. 2017. Selection indices for agronomic traits in segregating populations of soybean. Cienc Agron. 48(1): 110-117. https://doi.org/10.5935/1806-6690.20170012
Burdon RD, Li Y. 2019. Genotype–environment interaction involving site differences in expression of genetic variation along with genotypic rank changes: simulations of economic significance. Tree Genet Genomes. 15: 2. https://doi.org/10.1007/s11295-018-1308-3
Céron-Rojas JJ, Crossa J. 2018. Linear selection indices in modern plant breeding. Cham., Switzerland: Springer. https://doi.org/10.1007/978-3-319-91223-3
Delacy IH, Basford KE, Cooper M, Bull JK, McLaren CG. 1996. Analysis of multi-environment trial: an historical perspective. In: Cooper M, Hammer GL (eds.). Plant adaptation and crop improvement. Wallingford, UK: CAB International, pp. 39-124.
FAO. 2022. FAOSTAT. Food and Agriculture Organization of the United Nations, Rome, Italy. https://www.fao.org/faostat/en/#home
Foundra MZ, Hernandez M, Lopez R, Fernandez L, Sánchez A, López J, Ravelo I. 2000. Analysis of the variability in collected peanut (Arachis hypogea L.) cultivars for the establishment of core collections. Plant Genet Resour Newsl. 137: 9-13.
Golaktya PR, Makne VG. 1991. Genetic diversity in Spanish bunch groundnut. J. Maharashtra Agric Univ. 16(3): 337-339.
Hazel LN. 1943. The genetic basis for constructing selection indexes. Genetics. 28(6): 476-490. https://doi.org/10.1093/genetics/28.6.476
Jahufer MZZ, Casler MD. 2015. Application of the Smith-Hazel selection index for improving biomass yield and quality of switchgrass. Crop Sci. 55(3): 1212-1222. https://doi.org/10.2135/cropsci2014.08.0575
Hemadesh I, Ahmadi J, Fabriki-Ourang S, 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. Iran J Genet Plant Breed. 10(1): 11-30. https://doi.org/10.30479/IJGPB.2022.17153.1315
Henderson CR. 1984. Applications of linear models in animal breeding. Guelph, Ont.: University of Guelph.
Holland JB. 2006. Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Sci. 46(2): 642-654. https://doi.org/10.2135/cropsci2005.0191
Liu BH, Knapp S, Birkes D. 1997. Sampling distributions, biases, variances, and confidence intervals for genetic correlations. Theor Appl Genet. 94(1): 8-19. https://doi.org/10.1007/s001220050375
Maranna S, Nataraj V, Kumawat G, Chandra S, Rajesh V, Ramteke R, Manohar Patel R, Ratnaparkhe MB, Husain SM, Sanjay Gupta S, et al. 2021. Breeding for higher yield, early maturity, wider adaptability and waterlogging tolerance in soybean (Glycine max L.): A case Study. Sci Rep. 11: 22853. https://doi.org/10.1038/s41598-021-02064-x
Nobahar A, Mostafavi Rad M, Zakerin HR, Sayfzadeh S, Valadabady AR. 2019. Growth characteristics and seed yield of peanut (Arachis hypogea L.) as affected by topping height and application methods of zinc and calcium nano chelates. Seed Plant Prod J. 35(2): 183-201 (In Persian with English abstract). https://doi.org/10.22092/SPPJ.2020.122370.1041
Olivoto T, Nardino M. 2020. MGIDI: toward an effective multivariate selection in biological experiments. Bioinformatics. 37(10): 1383-1389. https://doi.org/10.1093/bioinformatics/btaa981
Pour-Aboughadareh A, Poczai P. 2021. A dataset on multi-trait selection approaches for screening desirable wild relatives of wheat, Data Brief. 39: 107541. https://doi.org/10.1016/j.dib.2021.107541
Pour-Aboughadareh A, Sanjani S, Nikkhah-Chamanabad H, Mehrvar MR, Asadi A, Amini A. 2021. Identification of salt-tolerant barley genotypes using multiple-traits index and yield performance at the early growth and maturity stages. Bull Natl Res Cent. 45: 117. https://doi.org/10.1186/s42269-021-00576-0
Safari P, Honarnezhad R, Esfahani M. 2008. Assessment of genetic variation in peanuts (Arachis hypogaea L.) cultivars using canonical discriminant analysis. Iran J Field Crops Res. 6(2): 327-334 (In Persian with English abstract). https://doi.org/10.22067/GSC.V6I2.2439
Samadi Gorji M, Mirabadi AZ, Forozzan K, Haghpanah M. 2019. Assessment of genetic diversity in groundnut (Arachis hypogea L.) germplasm using morphological traits. Plant Genet Res. 5(2): 85-94 (In Persion with English abstract). https://doi.org/10.29252/pgr.5.2.85
Searle SR, Casella G, McCulloch CE. 1992. Variance components. New York: Wiley. https://doi.org/10.1002/9780470316856
Smith HF. 1936. A discriminant function for plant selection. Ann Eugen. 7(3): 240-250. https://doi.org/10.1111/j.1469-1809.1936.tb02143.x
Solat Petloo N, Asghari Zakaria R, Ebadi A, Sharifi Ziveh P. 2022. Selection of cow cockle (Vaccaria hispanica) ecotypes based on agronomic traits under different irrigation regimes. J Crop Breed. 14(43): 135-144. (In Persian with English abstract). https://doi.org/10.52547/jcb.14.43.135
Vahedi I, Asghari Zakaria R, Zare N. 2023. Evaluation of salinity tolerance of cow cockle (Vaccaria hispanica) ecotypes at germination and adult plant stages. J Plant Physiol Breed. 13(2): 43-60. https://doi.org/10.22034/JPPB.2023.55239.1294
Vange T, Maga TJ. 2014. Genetic characteristics and path coefficient analysis in ten groundnut varieties (Arachis hypogaea L.) evaluated in the Guinea Savannah agro-ecological zone. Afr J Agric Res. 9(25): 1932-1937. https://doi.org/10.5897/AJAR2013.7340
Yue H, Olivoto T, Bu J, Li J, Wei J, Xie J, Chen S, Peng H, Nardino M, Jiang X. 2022a. Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques. Front Plant Sci. 13: 1-17. https://doi.org/10.3389/fpls.2022.1030521
Yue H, Wei J, Xie J, Chen S, Peng H, Cao H, Bu J, Xuwen Jiang X. 2022b. A study on genotype-by-environment interaction analysis for agronomic traits of maize genotypes across Huang-Huai-Hai region in China. Phyton-Int J Exp Bot. 91(1): 57-81. https://doi.org/10.32604/phyton.2022.017308
Zali H, Sofalian O, Hasanloo T, Asgharii 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.
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