آمار
| تعداد نشریات | 43 |
| تعداد شمارهها | 1,253 |
| تعداد مقالات | 15,411 |
| تعداد مشاهده مقاله | 51,245,857 |
| تعداد دریافت فایل اصل مقاله | 14,254,023 |
Evaluation of genotype × environment interaction for grain yield of promising genotypes of rice (Oryza sativa L.) derived from mutation induction using the GGE-biplot method | ||
| Journal of Plant Physiology and Breeding | ||
| دوره 10، شماره 2، اسفند 2020، صفحه 69-76 اصل مقاله (592.01 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/jppb.2020.13269 | ||
| نویسندگان | ||
| Gholamreza Cheloei1؛ Gholam Ali Ranjbar* 1؛ Nadali Babaeian-Jelodar1؛ Nadali Bagheri1؛ Mohammadzaman Nouri2 | ||
| 1Department of Plant Breeding, Faculty of Crop Sciences, Sari Agricultural Sciences and Natural Resources University, Sari, Iran. | ||
| 2Rice Research Institute of Iran, Amol, Iran. | ||
| چکیده | ||
| The existence of genotype × environment interaction complicates the evaluation of cultivar performance and reduces gain to selection. One of the multivariate methods for interpreting genotype by environment interaction is GGE-Biplot, in which the main effect of genotype and genotype by environment interaction are investigated simultaneously. In this study, 13 mutant genotypes of rice along with three check cultivars Tarrom-Mahalli, Tarrom-Jelodar and Neda were evaluated for grain yield stability in the two locations of Sari and Tonekabon during the years 2016 and 2017 using randomized complete block design with three replications within each environment. The results of GGE-biplot analysis showed that the two first components explained 92.52% of the total yield variation. According to the polygon view, all four environments of the experiment were located in the place that the Neda cultivar was at the top. Genotypes 33, 30, 26, 31 were highly stable genotypes and genotypes 18, 16 and 25 were highly unstable. In this study, we found only one mega-environment. Also following Neda and Jelodar cultivars, genotype 31 was closest to the ideal genotype. Ton 95 was the most desirable environment. | ||
| کلیدواژهها | ||
| Genotype by environment interaction؛ GGE-biplot؛ Mutant rice؛ Stability | ||
| مراجع | ||
|
Ahmadi J, Vaezi B and Fotokian MH, 2012. Graphical analysis of multi-environment trials for barley yield using AMMI and GGE-Biplot under rain-fed conditions. Journal of Plant Physiology and Breeding 2(1): 43-54.
Bana RS, Singh D, Nain MS, Kumar H, Kumar V and Sepat S, 2020. Weed control and rice yield stability studies across diverse tillage and crop establishment systems under on-farm environments. Soil & Tillage Research 204: 104729.
Bii CL, Ngug K, Kimani JM, George N and Chemining W, 2020. Genotype by environment analysis of rice (Oryza sativa L.) populations under drought stressed and well-watered environments. Australian Journal of Crop Science 14: 259-262.
Chandrashekhar S, Babu R, Jeyaprakash RU, Bhuvaneshwari K and Manonmani S, 2020. Yield stability analysis in multi-environment trials of hybrid rice (Oryza sativa L.) in northern India using GGE Biplot analysis. Electronic Journal of Plant Breeding 2: 665-673.
Jadhav S, Balakrishnan D, Shankar G, Beerelli K, Chandu G and Neelamraju S, 2019. Genotype by environment (G×E) interaction study on yield traits in different maturity groups of rice 22: 425-449.
Karimizadeh R, Mohammadi M and Sabaghnia N, 2013. Site regression biplot analysis for matching new improved lentil genotypes into target environments. Journal of Plant Physiology and Breeding 3(2): 51-65.
Kroonenberg PM, 1995. Introduction to biplots for G × E Tables. Department of Mathematics, Res. Rep. 51. University of Queensland, Australia.
Li Y, Suontama M, Burdon RD and Dungey HS, 2017. Genotype by environment interactions in forest tree breeding: review of methodology and perspectives on research and application. Tree Genetics & Genomes 13: 1-18.
Oladosu Y, Rafli MY, Abdullah N, Magaji U, Miah G, Hussin G and Ramli A, 2017. Genotype × environment interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia. Acta Agriculturae Scandinavica, Section B- Soil & Plant Science 7: 590-606.
R Development Core Team, 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. Austria. URL http://www.R-project.org/.
Raza Khan RA, Ramzan M, Haider Z, Akter M, Riaz M, Ali SS, Awan TH and Mahmood A, 2019. Stability and adaptability analysis in advance fine grain rice (Oryza sativa L.) genotypes for yield. Journal of Agriculture and Aquaculture 2: 1-9.
Yan W, 2002. Singular-value partitioning in biplot analysis of multi-environment trial data. Agronomy Journal 94: 990-996.
Yan W, 2019. LG biplot: a graphical method for mega-environment investigation using existing crop variety trial data. Scientific Reporters 9: 7130. doi.org/10.1038/s41598-019-43683-9.
Yan W, Cornelius PL, Crossa J and Hunt LA, 2001. Two types of GGE biplots for analyzing multi-environment trial data. Crop Science 41: 656-663.
Yan W, Hunt LA, Sheng Q and Szlavnics Z, 2000. Cultivar evaluation and mega environment investigations based on the GGE biplot. Crop Science 40: 597-605.
Yan W and Kang MS, 2003. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. CRC Press, Boca Raton, FL, USA, 271p.
Yan W and Rajcan, 2002. Biplot analysis of test sites and trait relations of soybean in onatario. Crop Science 42: 11-20.
Yan W and Tinker NA, 2006. Biplot analysis of multi-environment trial data: principles and applications. Canadian Journal of Plant Science 86: 623-645. | ||
|
آمار تعداد مشاهده مقاله: 355 تعداد دریافت فایل اصل مقاله: 303 |
||