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Mohammadi, Mohsen; Karimizadeh, Rahmatollah; Hosseinpour, T; FALAHI, Hosein Ali; Khanzadeh, Hassan; SABAGHNIA, Naser; MOHAMMADI, Pedram; ARMION, Mohammadd; HOSNI, Maghsoud Hasanpour

doi:10.15835/nsb437807

Cited by 10 publications

(10 citation statements)

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“…In this assay, the four above-mentioned measures had the highest efficiency for introducing high yield and stable hybrids simultaneously. Pourdad (2010) in safflower and Mohammadi et al (2010) in wheat reported high positive rank correlation between the Pi, GAI and mean yield, and they can be helpful for identifying high yield and stable genotypes; also, Karimzadeh et al (2012) reported that mean yield and the Di 2 and DI methods were in the same group of stability measures in durum wheat and can also be used for genotype selections based on high performance and stability. The coefficients of FW, FP, PJ, bi and ai regression models were in the second group (Group II); they were in the dynamic region and also placed near the mean yield area of biplot.…”

Section: Discussionmentioning

confidence: 99%

“…The coefficients of FW, FP, PJ, bi and ai regression models were in the second group (Group II); they were in the dynamic region and also placed near the mean yield area of biplot. Mohebodini et al (2006), Dehghani et al (2008), Karimzadeh et al (2012) and Mohammadi et al (2012) reported that the coefficients of regression models benefit from the dynamic concept of stability.…”

Section: Discussionmentioning

confidence: 99%

“…Francis and Kanenberg's (1978) coefficient of variability indicates the general CV% of each genotype as a stability measure (Lin and Binns 1986). Wricke's (1962) equivalence (W 2 ) reveals the GE interaction for genotype (i) across all environments squared and summed as a genotype stability measure (Lin and Binns 1986;Mohammadi et al 2012). Lin and Binns (1988) introduced the superiority index (Pi) as the general superiority of genotype and defined it as the mean square distance between the genotypes' response and the maximum response at all locations .…”

Section: Methodsmentioning

confidence: 99%

“…Due to the high correlation of (S y(l) 2 a) and (S y(l) 2 b) with environmental variance, these two methods could be appropriate alternatives for static concept of stability methods and also because of more consistent results, S y(l) 2 b measure with static concept of stability methods was more efficient than S y(l) 2 a. Mohammadi et al (2012) in wheat and Karimzadeh et al (2012) in durum wheat reported high positive rank correlation between the EV and S 2 di. Lin et al (1986) stated that the types I and IV stability (S y(l) 2 a and S y(l) 2 b) are grouping in static concept of stability.…”

Section: Changizi Et Al * G)e Interactions and Stability Of Corn Hybmentioning

confidence: 99%

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Evaluation of genotype×environment interaction and stability of corn hybrids and relationship among univariate parametric methods

et al. 2014

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Changizi, M., Choukan, R., Heravan, E. M., Bihamta, M. R. and Darvish, F. 2014. Evaluation of genotype×environment interaction and stability of corn hybrids and relationship among univariate parametric methods. Can. J. Plant Sci. 94: 1255–1267. There have been many approaches available in multi-location crop variety trial. However, the relationship among these approaches is not understood. In this study, therefore, grain yields of 16 corn hybrids were measured in 12 locations in Iran in 2011 and 2012 in order to compare the 23 parametric methods and to assess stability and adaptability of the hybrids. The combined ANOVA indicated that variances due to the genotypes, environments and genotype×environment interaction were substantially significant, which represents great variation among them. Principal component analysis based on rank correlation matrix indicated that stability methods can be classified into four groups. The group related to the dynamic concept and strongly associated with mean grain yield consisted of the measures, superiority index (Pi), desirability index (DI), geometric adaptability index (GAI) and genotypic stability (Di2 ). This group was more useful in agronomic goals in comparison with other methods. The second group also indicated the dynamic concept contained slope of regression models. The third group reflected the static concept included, the environmental variance (EV), the variance in regression deviation (S2di) and type IV stability concept ([Formula: see text]). The fourth group impressed concurrently by grain yield and stability included the measures coefficient of variability (CV), Wrick's ecovalence (W2), Shukla's stability variance (SH), Plaisted and Peterson's parameter (pp59), Plaisted's parameter (p60), yield reliability index (Ii), residual MS of regression models and coefficient of determination (R 2). Based on both concepts of stability (dynamic and static), hybrids (KLM76002/3×MO17), (KLM77002/10-5-1×K19/1) and (K47/2×MO17) were the most stable and (KSC704), (KSC720 (K74/1×K19)) and (K48/3×K18) were found to be the most adaptable to favorable environments. The methods of Pi, Di 2 , DI and GAI were more useful and more convenient than other methods. [Formula: see text] and [Formula: see text] showed an acceptable static concept of stability methods whereas study [Formula: see text] was more efficient than [Formula: see text].

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Changizi Et Al * G)e Interactions and Stability Of Corn Hybmentioning

confidence: 99%

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Evaluation of genotype×environment interaction and stability of corn hybrids and relationship among univariate parametric methods

et al. 2014

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“…En este estudio, el índice de selección Si (1) no mostró correlación de rango significativa con el RENFI, los seis índices de estabilidad y tampoco con los otros tres índices de selección simultánea, sugiriendo que es un método que involucra estimaciones y conceptos diferentes de selección al de sus similares. Estos resultados son similares a los reportados en algodón y otras especies cultivadas (León, 1986;Kang y Pham 1991;Espitia et al 1993;Espitia et al 2003;Mahommadi et al 2009;Farshadfar et al 2012Farshadfar et al , 2013Mohammadi et al 2012;Tadege et al 2014;Gedif et al 2014).…”

Section: El íNdice De Selección S Iunclassified

Correlación entre rendimiento, estabilidad fenotípica y métodos de selección simultánea en algodón (Gossypium hirsutum L.)

2017

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El rendimiento y la estabilidad fenotípica son necesarios para la liberación de nuevos cultivares. El objetivo de este estudio fue estimar la correlación lineal y de rango entre el rendimiento de fibra (RENFI), seis índices de estabilidad: S2i (Roemer), σ2va (Plaisted y Peterson), bi y S2di (Eberhart y Russell), Wi (Wricke), σ2i (Shukla), y cuatro índices de selección simultánea por RENFI y estabilidad: Pi (Lin y Binns), ER y SH (Eskridge) y Si(1) (Huehn), en 10 genotipos (G) de algodón, evaluados en seis ambientes (A) del Caribe Húmedo Colombiano. Se usó un diseño de bloques completo al azar por ambiente. El análisis de varianza combinado detectó diferencias altamente significativas para ambientes e interacción GxA. El RENFI no mostró correlación lineal con los índices de estabilidad y de selección simultánea (r>-0,43), excepto con SH (r=0,65*). Los seis índices de estabilidad presentaron correlación lineal positiva y significativa entre todos (r≥0,71*), excepto bi, que sólo mostró correlación con S2i (r=0,96**). El RENFI unicamente mostró correlación de rango (rs=0,76**) con el S2i. El índice σ2va presentó correlación de rango significativa y positiva (rs>0,68) con S2di, σ2i, Wi, ER y SH. La clasificación por estabilidad de los genotipos fue igual y perfecta (rs=1,0**) cuando se utilizó σ2va, S2di, σ2i y Wi. La correlación de rango entre los índices de selección simultánea sólo fue similar (rs>0,82**) entre ER, SH y Pi. Los índices de selección simultánea ER y SH favorecieron la selección, preferencialmente, por estabilidad, dada su rs>0,66 significativa y directa con σ2va, S2di, σ2i and Wi.

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Grain yield stability analysis using parametric and nonparametric statistics in oat (Avena sativa L.) genotypes in Ethiopia

Kebede,

Worku,

Jifar

et al. 2023

Grassland Research

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BackgroundThe performance of oat genotypes differs across environments due to variations in biotic and abiotic factors. Thus, evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement.MethodsThe study aimed to assess the interaction (genotype‐by‐environment interaction; GEI) effect and determine the stability of grain yield in oat (Avena sativa L.) genotypes in Ethiopia using parametric and nonparametric stability statistics. Twenty‐four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times.ResultsThe pooled analysis of the variance of grain yield showed significant variations among genotypes, environments, and their interaction effects. Significant GEI revealed the rank order change of genotypes across environments. The environment main effect captured 44.62% of the total grain yield variance, while genotype and GEI effects explained 28.84% and 26.54% of the total grain yield variance, respectively. The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics. The results indicated that genotypes with superior grain yield‐ showed stable performance on the basis of the stability parameters of the genotypic superiority index (Pi), the Perkins and Jinks adjusted linear regression coefficient (Bi), and the yield stability index (YSI), indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes. Spearman's rank correlation coefficients also showed that the stability parameters of Pi, Bi, and YSI had a significant positive association with grain yield. However, grain yield had an inverse correlation with the stability parameters of standard deviation, deviation from regression , the Hernandez desirability index (Dji), Wricke ecovalence (Wi), the Shukla stability variance (σi2), the AMMI stability value (ASV), and environmental variance , indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low‐yielding genotypes more, compared to high‐yielding ones.ConclusionsTherefore, G5, G8, G11, G12, G14, G16, G17, G19, and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi, Bi, and YSI, and selection of these superior genotypes would improve grain yield in oat genotypes. However, the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.

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Cited by 10 publications

References 0 publications

Evaluation of genotype×environment interaction and stability of corn hybrids and relationship among univariate parametric methods

Evaluation of genotype×environment interaction and stability of corn hybrids and relationship among univariate parametric methods

Correlación entre rendimiento, estabilidad fenotípica y métodos de selección simultánea en algodón (Gossypium hirsutum L.)

Grain yield stability analysis using parametric and nonparametric statistics in oat (Avena sativa L.) genotypes in Ethiopia

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