Spike stability parameters in wheat grown on solonetz soil

Petrović, Sonja; Dimitrijević, Miodrag; Belić, Milivoj; Banjac, Borislav; Vukosavljev, M.

doi:10.2298/gensr0902199p

Cited by 11 publications

(9 citation statements)

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“…Grain weight per spike, together with numerous traits, contributes to the formation of total grain yield (KNEZEVIC et al, 2015). Therefore, this trait can be included in the indirect indicators of grain yield (PETROVIC et al, 2009). According to ROHANI and MARKER (2016b) correlation between yield and its components presents a confusing picture due to interrelationships between component characteristics by themselves.…”

Section: Introductionmentioning

confidence: 99%

Variability, correlation, path analysis and stepwise regression for yield components of different wheat genotypes

et al. 2018

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In order to evaluate the variability and relationship between different wheat yield components, a randomized complete block design experiment with ten genotypes of wheat had been carried out during three growing seasons (2010-2012). The number of spikelet per spike and grain weight per spike had low genotypic and phenotypic variability, while plant height had the highest one. High heritability was observed for plant height (h 2 =93.1%), spike length (h 2 =92.3%) and spike density (h 2 =92.9%). The low heritability was found for grain weight per spike (h 2 =35.6%). Grain weight per spike was in significant positive genotypic and phenotypic correlation with all the traits (plant height, spike height, number of spikelet per spike, number of grain per spike and spike weight) except spike density. The spike weight had the highest phenotypic (r p =0.988), while number of spikelet per spike had the highest genotypic correlation with grain weight per spike (r g =0.981). Path coefficient analysis revealed that all the traits had highly significant direct effect on grain weight per spike, except spike length. The stepwise regression revealed that 87.1% of the grain weight per spike variation was explained by model which excludes spike length. Spike weight and plant height had the highest shared and unique contribution to grain weight per spike.

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Section: Introductionmentioning

confidence: 99%

Variability, correlation, path analysis and stepwise regression for yield components of different wheat genotypes

et al. 2018

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“…The AMMI model is utilized for three main purposes (Gauch, 1988;Crossa et al, 1990): (i) to suitable in the initial statistical analyses of yield experiments, (ii) to summarize the relationships between genotypes and environments (GE) and (iii) to applicable for understanding complex genotypes × environment interaction effects. AMMI analysis has been applied extensively with great success to interpret genotype × environment interaction in wheat (Petrovic et al, 2009;Petrovic et al, 2010;Mahmoodi et al, 2011;Oikeh et al, 2004;Mohammadi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Stability of Grain Yield of Durum Wheat Genotypes by Ammi Model

Mohtasham¹,

Sharifi²,

Karimizadeh³

et al. 2015

AgricultForest

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SUMMARYQuick seed germination and stand establishment are significant factors to lentil This study was performed to determine the yield performances of 20 durum wheat genotypes across five sites in warm dryland areas of Iran, during 2004-2006 growing seasons. The experimental design was a randomized complete block design with four replications. The combined analysis of variance indicated that effects of environments, genotypes and genotype × environment (GE) interactions were significant, suggesting differential responses of the genotypes to environments and the need of stability analysis. Analysis of variance for AMMI (additive main effects and multiplicative interactions) model revealed the first four interaction principal components (IPC 1, IPC 2, IPC 3, IPC 4) were highly significant by Gollob's F-test and accounted for 78.32% of GE interaction. The genotypes have high variation around the mean yield (3358.68 kg ha-1). According to IPC1, genotypes G17, G3, G1 and G8 were the most stable genotypes. Based on EV4 parameter, genotypes G17, G8, G12, and G18 and according to EV1, genotypes G4, G6, G19, G7 and G20 were the most stable genotypes. Genotypes G10, G9, G4, G14, G11, G6 and G18 based on SIPC4 parameter and genotypes G8, G1, G12, G18 and G17 based on ASV parameter were the most stable genotypes. MASV indicated that G17, G12, G8, G18, G1 and G3 were most stable genotypes. Overall based on the all AMMI parameters, genotypes G3, G8, G1, G12, G17 and G18 were the most stable genotypes which had relatively high mean yield and can be used as superior genotypes in relation with the warm dryland environments.

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“…Deitos et al (2006) indicate that G×E interaction is important in breeding process because it influences the genetic gain, as well as the recommendation and choosing the varieties with high adaptability. Petrović et al (2009) concluded that breeding for the targeted environment highly depends on the identification of the major sources of phenotypic variation in that region. In order to develop a variety or a hybrid which possess lower G×E interaction, for the dominant sources of variation, variety should have the balanced proportion between stabile and high yield (Boakyewaa, 2012).…”

Section: Academicpresmentioning

confidence: 99%

Stability of the expression of the maize productivity parameters by AMMI models and GGE-biplot analysis

Božović

Popović

Rajičić

et al. 2020

Not Bot Horti Agrobo

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The objective of this study was to estimate genotype by locality, by year, by treatments (G×LxYxT) interaction using AMMI model, to identify maize genotypes with stable number of rows of grains performance in different growing seasons. The trials conducted with seven maize lines/genotypes, four treatments, two years and at the two locations. The results showed that the influence of genotype (G), year (Y), locality (L), and G×L, G×T, G×L×T, G×Y×T, G×Y×L×T interaction on maize number of rows of grains were significant (p<0.01). The genotype share in the total phenotypic variance for the grains number rows of was 53.50%, and the interaction was 21.15%. The results also show that the sums of the squares of the first and second major components (PC1 and PC2) constitute 100% of the sum of the squares of the interaction G×L. The first PC1 axis belongs to all 100%, which points to the significance of the genotype in the total variation and significance of the genotype for overall interaction with other observed sources of variability. The highest stability in terms of expression of the grains number of rows had the genotype L-6, followed by the genotypes L-4, L-5 and L-3. The lowest stability was demonstrated by the genotypes L-2 and L-1, which confirmed that these genotypes are not important for further selection in terms of this trait.

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Spike stability parameters in wheat grown on solonetz soil

Cited by 11 publications

References 1 publication

Variability, correlation, path analysis and stepwise regression for yield components of different wheat genotypes

Variability, correlation, path analysis and stepwise regression for yield components of different wheat genotypes

Stability of Grain Yield of Durum Wheat Genotypes by Ammi Model

Stability of the expression of the maize productivity parameters by AMMI models and GGE-biplot analysis

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