Hassan Amiri Oghan, Behnam Bakhshi, V. Rameeh, A. Faraji, A. Askari, H. Fanaei
{"title":"The Efficiency Of Stability Analysis To Identify High-Yielding And Stable Oilseed Rape Genotypes ","authors":"Hassan Amiri Oghan, Behnam Bakhshi, V. Rameeh, A. Faraji, A. Askari, H. Fanaei","doi":"10.17557/tjfc.1055496","DOIUrl":null,"url":null,"abstract":"One of the complex issue in the way of releasing new high-yielding and stable oilseed rape cultivars is genotype by environment interaction (GEI) which reduce selection efficiency. In the current study, parametric and non-parametric statistics as well as the AMMI model have been compared to identify the best stability models to clarify GEI complexity. The experiment has been conducted in the warm regions of Iran including; Gorgan, Sari, Zabol, and Hajiabad during two cropping seasons (2016-2017 and 2017-2018) for 16 genotypes in a randomized complete block design with three replications. The AMMI analysis of variance on grain yield showed the significant effects of genotype, environment, and the interaction effects of GEI on yield. Based on the AMMI ANOVA, the major contribution of GEI was captured by the first and second interaction principal component axes (IPCA1 and IPCA2) which explained 34.29% and 29.81% of GEI sum of the square, respectively. Additionally, Different parametric and non-parametric stability methods including; bi, S2di, CVi, W2i, σ2i, Pi, Si(1), Si(2), Si(3), Si(6), Npi(1), Npi(2), Npi(3), Npi(4), KR and TOP have also investigated. Based on AMMI, parametric, and non-parametric stability statistics, genotypes G2 (SRL-95-7) and G9 (SRL-95-16) were selected as the stable and high-yielding genotypes. Likewise, Principal component analysis based on rank correlation matrix enabled us to distinguish high-yielding genotypes to stable (high-yielding genotypes in various environments) and unstable (high-yielding genotypes in low-yielding environments) ones. Furthermore, a significant Spearman correlation was observed between yield mean and GSI, Pi, Si(3), Si(6), Npi(3), Npi(4), and KR. Therefore, different efficient strategies were identified in this study and since we looked up high-yielding and stable genotypes, G2 (SRL-95-7) and G9 (SRL-95-16) were finally selected.","PeriodicalId":23385,"journal":{"name":"Turkish Journal of Field Crops","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Turkish Journal of Field Crops","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.17557/tjfc.1055496","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 1
Abstract
One of the complex issue in the way of releasing new high-yielding and stable oilseed rape cultivars is genotype by environment interaction (GEI) which reduce selection efficiency. In the current study, parametric and non-parametric statistics as well as the AMMI model have been compared to identify the best stability models to clarify GEI complexity. The experiment has been conducted in the warm regions of Iran including; Gorgan, Sari, Zabol, and Hajiabad during two cropping seasons (2016-2017 and 2017-2018) for 16 genotypes in a randomized complete block design with three replications. The AMMI analysis of variance on grain yield showed the significant effects of genotype, environment, and the interaction effects of GEI on yield. Based on the AMMI ANOVA, the major contribution of GEI was captured by the first and second interaction principal component axes (IPCA1 and IPCA2) which explained 34.29% and 29.81% of GEI sum of the square, respectively. Additionally, Different parametric and non-parametric stability methods including; bi, S2di, CVi, W2i, σ2i, Pi, Si(1), Si(2), Si(3), Si(6), Npi(1), Npi(2), Npi(3), Npi(4), KR and TOP have also investigated. Based on AMMI, parametric, and non-parametric stability statistics, genotypes G2 (SRL-95-7) and G9 (SRL-95-16) were selected as the stable and high-yielding genotypes. Likewise, Principal component analysis based on rank correlation matrix enabled us to distinguish high-yielding genotypes to stable (high-yielding genotypes in various environments) and unstable (high-yielding genotypes in low-yielding environments) ones. Furthermore, a significant Spearman correlation was observed between yield mean and GSI, Pi, Si(3), Si(6), Npi(3), Npi(4), and KR. Therefore, different efficient strategies were identified in this study and since we looked up high-yielding and stable genotypes, G2 (SRL-95-7) and G9 (SRL-95-16) were finally selected.
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