The Efficiency Of Stability Analysis To Identify High-Yielding And Stable Oilseed Rape ‎Genotypes ‎

IF 1 4区农林科学 Q3 AGRONOMY

Turkish Journal of Field Crops Pub Date : 2022-11-22 DOI:10.17557/tjfc.1055496

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.‎

查看原文本刊更多论文

稳定性分析法鉴定高产稳定油菜籽的有效性‎基因型‎

高产稳定油菜新品种选育中的一个复杂问题‎品种是环境交互作用基因型，降低了选择效率。在里面‎目前的研究、参数统计和非参数统计以及AMMI模型‎以确定最佳稳定性模型，从而阐明GEI的复杂性。这个‎实验在伊朗温暖地区进行，包括；Gorgan、Sari、Zabol，‎在两个种植季节（2016-2017和2017-2018）‎三次重复的随机完全区组设计。AMMI方差分析‎粮食产量表现出基因型、环境和相互作用的显著影响‎GEI对产量的影响。根据AMMI方差分析，GEI的主要贡献是‎由第一和第二交互主分量轴（IPCA1和IPCA2）捕获‎分别解释了34.29%和29.81%的GEI平方和。此外，‎不同的参数和非参数稳定性方法，包括：；bi、S2di、CVi、W2i、σ2i、Pi，‎还研究了Si（1）、Si（2）、Si3、Si6、Npi1、Npi2、Npi3、Npi4、KR和TOP。基于‎AMMI，参数和非参数稳定性统计，基因型G2（SRL-95-7）和G9‎‎（SRL-95-16）‎‏ ‏被选为稳定高产的基因型。同样，委托人‎基于秩相关矩阵的成分分析使我们能够区分高产‎基因型到稳定（在各种环境中的高产基因型）和不稳定（高-‎在低产环境中的高产基因型）。此外，重要的斯皮尔曼‎观察到产量平均值与GSI、Pi、Si（3）、Si（6）、Npi（3），Npi（4）和KR之间的相关性。‎因此，本研究确定了不同的有效策略‏ ‏自从我们抬起头来‎高产稳定基因型G2（SRL-95-7）和G9（SRL-95.16）‎‏ ‏终于‎选定。‎

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Turkish Journal of Field Crops AGRONOMY-

CiteScore

1.50

自引率

12.50%

发文量

审稿时长

>12 weeks

期刊介绍： Information not localized